Digital Commons Network^™

Modeling And Design Optimization For Membrane Filters, Yixuan Sun

Dissertations

Membrane filtration is widely used in many applications, ranging from industrial processes to everyday living activities. With growing interest from both industrial and academic sectors in understanding the various types of filtration processes in use, and in improving filter performance, the past few decades have seen significant research activity in this area. Experimental studies can be very valuable, but are expensive and time-consuming, therefore theoretical studies offer potential as a cost-effective and predictive way to improve on current filter designs. In this work, mathematical models, derived from first principles and simplified using asymptotic analysis, are proposed for: (1) pleated membrane …

Shale Softening Based On Pore Network And Laboratory Investigations, Di Zhang

Dissertations

This dissertation consists of two major parts: Firstly, experimental investigation of four major shale softening mechanisms and quantifications of structural parameters. Secondly, numerical simulations of nano-scale flow behaviors using the previous experiments determined parameters based on modified pore network modeling.

Hydraulic fracturing is widely applied to economical gas production from shale reservoirs. Still, the gradual swelling of the clay micro/nano-pores due to retained fluid from hydraulic fracturing causes a gradual reduction of gas production. Four different gas-bearing shale samples are investigated to quantify the expected shale swelling due to hydraulic fracturing. These shale samples are subject to heated deionized (DI) …

Data-Driven Learning For Robot Physical Intelligence, Leidi Zhao

Dissertations

The physical intelligence, which emphasizes physical capabilities such as dexterous manipulation and dynamic mobility, is essential for robots to physically coexist with humans. Much research on robot physical intelligence has achieved success on hyper robot motor capabilities, but mostly through heavily case-specific engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous manner, robot learning from human demonstration (LfD) has achieved great progress, but still has limitations handling dynamic skills and compound actions. In this dissertation, a composite learning scheme which goes beyond LfD and integrates robot learning from human definition, demonstration, and evaluation is proposed. This method tackles …

Participatory Learning: Measuring Learning And Educational Technology Acceptance, Erick Sanchez Suasnabar

Dissertations

Participatory Learning (PL) integrates several learning approaches, engaging students throughout the entire assignment process for both online and face-to-face courses. Beyond simply providing a solution, students also craft a problem (problem-based learning), grade each other (peer assessment and feedback), evaluate themselves (self-assessment), and can view others’ work (learning by example). This dissertation research explores the resulting learning effects. Contributions to both educational and Information Systems research include extending an early PL model and experiments that applied the PL approach to examinations, by validating and testing new constructs based on user activity and critical thinking. In addition, the study explores a …

Analysis Of Container Throughput: Demand Forecast And Seaport Competitiveness Assessment, Hussain Talat Sulaimani

Dissertations

Seaports play a crucial role in the container industry, where they act as important nodes in the transport chain to facilitate international trade. In a competitive market, port capacity plays a significant role in defining its competitive position to attract demand and avoid congestion. Failing to provide suitable capacity results in the loss of market share. Therefore, port decision-makers face the challenge of maintaining and developing suitable port facilities to provide efficient services to port users. One of the aspects that decision-makers consider in the planning and development process is analyzing container demand. The analysis of container demand can be …

Designing Collaborative Lifelogging To Facilitate Learning In Collaborative Physical-Recreation Communities, Sayed Mousa Ahmadi Olounabadi

Dissertations

Since the 1940s, researchers have envisioned lifelogging as the systematic capture and utilization of lived experiences for augmenting learning, performance, and community Unfortunately, this vision was never actualized since few, if any, systems support lifelogging in the term's original sense. Technologies that emerged through the Quantified-Self (QS) movement allowed users to monitor and track almost every life aspect. However, the decontextualized self-tracking data QS systems produced are unsuitable for supporting learning and community engagement, and therefore have not made lifelogging a reality yet. Central to this dissertation is understanding how to augment learning and community through lifelogging. This is particularly …

Essays On Health Care Quality: Timeliness, Equity, And Efficiency, Abubakar-Sadiq Bouda Abdulai

Dissertations

According to the National Academy of Medicine (NAM) (formerly called the Institute of Medicine), a quality health care system embodies six attributes: timeliness, equity, safety, efficiency, effectiveness, and patient-centeredness. Timeliness is to avoid unnecessary delays in care delivery for patients and caregivers; equity is to ensure that the quality of care that patients receive does not vary based on their personal characteristics; safety is to ensure that the care that is intended to help patients does not harm them; efficiency is to avoid waste and optimize resource allocation to improve care delivery; effective care is one that relies on sound …

Reserve Price Optimization In Display Advertising, Achir Kalra

Dissertations

Display advertising is the main type of online advertising, and it comes in the form of banner ads and rich media on publishers' websites. Publishers sell ad impressions, where an impression is one display of an ad in a web page. A common way to sell ad impressions is through real-time bidding (RTB). In 2019, advertisers in the United States spent nearly 60 billion U.S. dollars on programmatic digital display advertising. By 2022, expenditures are expected to increase to nearly 95 billion U.S. dollars. In general, the remaining impressions are sold directly by the publishers. The only way for publishers …

Constructive Solution Methodologies To The Capacitated Newsvendor Problem And Surrogate Extension, Pinyuan Shan

Dissertations

The newsvendor problem is a single-period stochastic model used to determine the order quantity of perishable product that maximizes/minimizes the profit/cost of the vendor under uncertain demand. The goal is to fmd an initial order quantity that can offset the impact of backlog or shortage caused by mismatch between the procurement amount and uncertain demand. If there are multiple products and substitution between them is feasible, overstocking and understocking can be further reduced and hence, the vendor's overall profit is improved compared to the standard problem. When there are one or more resource constraints, such as budget, volume or weight, …

Advances In Deep Learning With Applications To Computer Vision And Astronomy, Zhihang Hu

Dissertations

Deep Learning has spanned a variety of applications in computer vision as well as computational astronomy. These two aspects obtained similar data structure, therefore, their solutions can be transferable between each other. This dissertation look into two video-related tasks in computer vision and propose a novel problem in computational astronomy.

Specifically, acquiring an in-depth understanding of videos has been a cornerstone problem in computer vision. This problem has been studied by various researchers from different perspectives, among which video prediction has attracted much attention. Video prediction aims to generate the pixels of future frames given a sequence of context frames. …

Learning Of Radar System For Target Detection, Wei Jiang

Dissertations

In this dissertation, the problem of data-driven joint design of transmitted waveform and detector in a radar system is addressed. Two novel learning-based approaches to waveform and detector design are proposed based on end-to-end training of the radar system. The first approach consists of alternating supervised training of the detector for a fixed waveform and reinforcement learning of the transmitter for a fixed detector. In the second approach, the transmitter and detector are trained simultaneously. Various operational waveform constraints, such as peak-to-average-power ratio (PAR) and spectral compatibility, are incorporated into the design. Unlike traditional radar design methods that rely on …

Exploring Fused Deposition Modeling (Fdm) Three-Dimensional Printing Tablet Design Options For Pharmaceutical Dosage Forms, Guluzar Gorkem Buyukgoz

Dissertations

This dissertation examines the use of Fused Deposition Modeling (FDM) based three-dimensional (3D) printing approach for developing patient-specific dosage forms and addressing related technical challenges in such drug delivery systems. The first main objective is to explore pharmaceutical tablet design options using novel FDM 3D printing technology as the drug delivery platform such that drug form and tablet properties are tailored by considering patient age-specific formulations and dissolution control. Of the five different design options, two proposed options meet the main objective of providing similar drug release, whereas the popular option of fixed drug concentration but differing tablet size could …

Novel Statistical Modeling Methods For Traffic Video Analysis, Hang Shi

Dissertations

Video analysis is an active and rapidly expanding research area in computer vision and artificial intelligence due to its broad applications in modern society. Many methods have been proposed to analyze the videos, but many challenging factors remain untackled. In this dissertation, four statistical modeling methods are proposed to address some challenging traffic video analysis problems under adverse illumination and weather conditions.

First, a new foreground detection method is presented to detect the foreground objects in videos. A novel Global Foreground Modeling (GFM) method, which estimates a global probability density function for the foreground and applies the Bayes decision rule …

Selective Neural Stimulation By Leveraging Electrophysiological Diversity And Using Alternative Stimulus Waveforms, Bemin Ghobreal

Dissertations

Efforts on finding the principle mechanism for selective neural stimulation have concentrated on segregating the neurons based on their size and other geometric factors. However, neuronal subtypes found in different parts of the nervous system also differ in their electrophysiological properties. The primary objective of this study is to investigate the feasibility of selective activation of neurons by leveraging the diversity seen in passive and active membrane properties.

Using both a local membrane model and an axon model based on the CRRSS, the diversity of electrophysiological properties is simulated by varying four model parameters (membrane leakage-Gleak and capacitance-Cm, temperature coefficient-Ktemp, …

Methods For Extending Biomedical Reference Ontologies And Interface Terminologies For Ehrr Text Annotation, Vipina Kuttichi Keloth

Dissertations

Biomedical ontologies and terminologies are a cornerstone in various electronic health record systems (EHRs) for encoding information related to diseases, diagnoses, treatments, etc. Ontologies in general represent entities (concepts) and events along with all interdependent properties and relationships in an efficient way to facilitate easy access, retrieval and sharing. With the landscape of medicine rapidly changing, biomedical ontologies and terminologies need to rapidly evolve to support interoperability, medical coding, record keeping, and healthcare activities in general, and to facilitate interdisciplinary research. Extending ontologies by identifying new and missing concepts plays a vital role in the maintenance of ontologies to keep …

Reconstituting The Cyanobacterial Circadian Clock In Vitro, Pyong Hwa Kim

Dissertations

Cyanobacteria are photosynthetic organisms that are known to be responsible for oxygenating Earth’s early atmosphere. Having evolved to ensure optimal survival in the periodic light/dark cycle on this planet, their genetic codes are packed with various tools, including a sophisticated biological timekeeping system. Among the cyanobacteria is Synechococcus elongatus PCC 7942, the simplest clock-harboring organism with a powerful genetic tool that enabled the identification of its intricate timekeeping mechanism. The three central oscillator proteins—KaiA, KaiB, and KaiC—drive the 24 h cyclic gene expression rhythm of cyanobacteria, and the "ticking" of the oscillator can be reconstituted inside a test tube just …

Intermittent Dynamics Of Dense Particulate Matter, Chao Cheng

Dissertations

Granular particle systems are scattered around the universe, and they can behave like solids when there exist strong force-bearing networks, so that the granular system can resist certain stress without deformation. When such a network is not present, particles yield to small stress and behave like a fluid. A wide range of systems exhibit intermittent dynamics as they are slowly loaded, with different dynamical regimes governing many industrial and natural phenomena. While a significant amount of research on exploring intermittent dynamics of granular systems has been carried out, not much is known about the connection between particle-scale response and the …

Radio Diagnostics Of Particles And Plasma In The Solar Corona, Sherry Chhabra

Dissertations

Radio diagnostics, in addition to their capabilities in exploring intense, impulsive bursts, also provide a high sensitivity to much weaker events, which may not show any substantial signature in other wavelengths.

The initial case study examines a complex event consisting of multiple radio sources/bursts associated with a fast coronal mass ejection (CME) and an M 2.1 class solar flare (SOL2015-09-20). ‘First-light’ data from the Owens Valley Radio Observatory–Long Wavelength Array is put in context with observations from Large Angle and Spectrometric Coronagraph onboard the Solar and Heliospheric Observatory, along with the WAVES radio spectrograph onboard WIND, the Expanded Owens Valley …

Development Of Deep Learning Neural Network For Ecological And Medical Images, Shaobo Liu

Dissertations

Deep learning in computer vision and image processing has attracted attentions from various fields including ecology and medical image. Ecologists are interested in finding an effective model structure to classify different species. Tradition deep learning model use a convolutional neural network, such as LeNet, AlexNet, VGG models, residual neural network, and inception models, are first used on classifying bee wing and butterfly datasets. However, insufficient data sample and unbalanced samples in each class have caused a poor accuracy. To make improvement the test accuracy, data augmentation and transfer learning are applied. Recently developed deep learning framework based on mathematical morphology …

Safe Yield Comparisons For The 1960’S And 2000’S Drought In The Passaic River Basin, Stephanie R. Santos

Dissertations

The Passaic River Basin is a complex system of rivers, tributaries, reservoirs, and some of the largest water purveyors in the State of New Jersey. Located in the northeastern part of the State, it drains approximately 800 square miles of water that is used for distribution to the five major purveyors: North Jersey District Water Supply Commission, Passaic Valley Water Commission, Jersey City, Newark, and New Jersey American Water. These purveyors operate under a safe yield, which is defined by the New Jersey Department of Environmental Protection (NJDEP) as the amount of water that should be continuously available should the …

First-Principles Density Functional Theory Studies On Perovskite Materials, Aneer Lamichhane

Dissertations

Perovskites are a family of materials with a diverse combination of different elements. As a consequence, they exhibit numerous functionalities such as pyroelectric, piezoelectric, ferroelectric, and ferromagnetic with applications in photovoltaic cells, LEDs, superconductivity, colossal magneto-resistance, and topological insulators. After 2009, perovskites have gained notoriety as suitable materials for solar cells and alternative candidates to silicon-based conventional solar cells. Generally, oxide perovskites exhibit good dielectric properties, halide perovskites display good photonic qualities, and chalcogenide perovskites are used in applications in solid-state lighting, sensing, and energy harvesting. In this dissertation, various types of perovskites ranging from oxide to halide are investigated …

Optimizing Work Zone Schedules Considering Traffic Diversion With Artificial Bee Colony Algorithm, Celina Semaan

Dissertations

Highway maintenance activities often decrease roadway capacity and intrude traffic movements. The need to finish the project on time and under a specific budget while minimizing the traffic congestion and complying with the emission standards requires an appropriate work zone schedule optimization. The objective of this research is to improve the efficiency of work zone activities and minimize the total project cost including maintenance, user, and emission cost.

While previous studies investigated the work zone optimization problem, they did not consider the implementation of emission standards nor applied a green diversion strategy. This dissertation analyzes the optimization of work zone …

Intelligent And Secure Fog-Aided Internet Of Drones, Jingjing Yao

Dissertations

Internet of drones (IoD), which utilize drones as Internet of Things (IoT) devices, deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing. Computing tasks are usually offloaded to the cloud data center for intensive processing. However, many IoD applications require real-time processing and event response (e.g., disaster response and virtual reality applications). Hence, data processing by the remote cloud may not satisfy the strict latency requirement. Fog computing attaches fog nodes, which are equipped with computing, storage and networking resources, to IoD gateways to assume a substantial amount of …

Impact Of User Traversal On Performance Of Stem Learners In Immersive Virtual Environments, Eric W. Nersesian

Dissertations

The emerging technologies of augmented and virtual reality (AR/VR) may have vast implications to societal communication and representation of information. AR/VR computer interfaces are unique in that they may be placed spatially around the user in three-dimensional (3D) space; this affords new methods of both presentation and user interaction with the target information.

This may be especially impactful in the education of science, technology, engineering, and mathematics (STEM) professionals. Prior research has shown that simulations and visualizations improve the performance of STEM learners compared to live instruction and textbook reading. Yet, research into AR/VR as a learning environment for widespread …

Deep Learning On Image Forensics And Anti-Forensics, Zhangyi Shen

Dissertations

Image forensics protect the authenticity and integrity of digital images. On the contrary, as the countermeasures of digital forensics, anti-forensics is applied to expose the vulnerability of forensics tools. Consequently, forensics researchers could develop forensics tools against possible new attacks. This dissertation investigation demonstrates two image forensics methods based on convolutional neural network (CNN) and two image anti-forensics methods based on generative adversarial network (GAN).

Detecting unsharp masking (USM) sharpened image is the first study in this dissertation. A CNN architecture comprises four convolutional layers and a classification module is proposed to discriminate sharpened images and unsharpened images. The results …

Eigenvalue Problems For Fully Nonlinear Elliptic Partial Differential Equations With Transport Boundary Conditions, Jacob Lesniewski

Dissertations

Fully nonlinear elliptic partial differential equations (PDEs) arise in a number of applications. From mathematical finance to astrophysics, there is a great deal of interest in solving them. Eigenvalue problems for fully nonlinear PDEs with transport boundary conditions are of particular interest as alternative formulations of PDEs that require data to satisfy a solvability condition, which may not be known explicitly or may be polluted by noisy data. Nevertheless, these have not yet been well-explored in the literature. In this dissertation, a convergence framework for numerically solving eigenvalue problems for fully nonlinear PDEs is introduced. In addition, existing two-dimensional methods …

Global Mhd And Acoustic Solar Modeling And Helioseismic Analysis, Andrey M. Stejko

Dissertations

Computational fluid dynamic simulations have become one of the most prolific avenues of study in the fields of solar and stellar physics within the last several decades. With the advent of ever increasing computing power, high-definition global models of the Sun have become indispensable in understanding the complex and chaotic nature of flows in the solar interior, as well as their impact on the evolution of the global solar dynamo. The mechanisms that connect the generation of the toroidal magnetic field at the base of the convection zone to the emergence of a poloidal field onto the solar surface can …

Electric Field Induced Self-Assembly Of Mesoscale Structured Materials And Smart Fluids, Suchandra Das

Dissertations

This dissertation aims to study the forces that drive self-assembly in binary mixtures of particles suspended in liquids and on fluid-liquid interfaces when they are subjected to a uniform electric or magnetic field. Three fluid-particle systems are investigated experimentally and theoretically : (i) Suspensions of dielectric particles in dielectric liquids; (ii) Suspensions of ferromagnetic and diamagnetic particles in ferrofluids; and (iii) Dielectric particles on dielectric fluid-liquid interfaces. The results of these studies are then used to estimate the parameter values needed to assemble materials with desired mesoscale microstructures.

The first fluid-particle system studied is an electrorheological (ER) fluid formed using …

Service Quality Assessment And Improvement Methods And Tools, Kevin M. Moriarty

Dissertations

The nucleus of this research concept and system is being applied to turret lathe and milling machine Computer Numerical Control (CNC) tool systems. The research has a generic application to the service of broad array of sophisticated computer controlled / integrated machines, devices / equipment such as industrial robotics, medical equipment, surgical robots, and similar types of engineered system. Quality design review for quality service systems is a unique concept. Standard product service systems are qualitative and subjective in nature. The quantitative system identifies Key Predictive Attributes (KPAs) and applies quantitative methods to these attributes to develop a systematic process …

Asymmetric Multivariate Archimedean Copula Models And Semi-Competing Risks Data Analysis, Ziyan Guo

Dissertations

Many multivariate models have been proposed and developed to model high dimensional data when the dimension of a data set is greater than 2 (d ≥ 3). The existing multivariate models often force the “exchangeable” structure for part or the whole model, are not very flexible which tends to be of limited use in practice. There is a demand for developing and studying multivariate models with any pre-specified bivariate margins.

Suppose there exists such a class of flexible models with any pre-specified bivariate margins. Given a multivariate data, what is the distribution function and how to easily estimate the parameters …