Engineering Commons^™

Removel Of Polluntants From Aqueous Solution Via Graphene Oxide/Magnesium Oxide Nanocomposites, Mahdi Heidarizad

Civil and Environmental Engineering Theses and Dissertations

In this study, a series of graphene oxide/magnesium oxide nanocomposites (GO/MgO NCs) were synthesized and applied for the removal of Methylene Blue (MB) from aqueous solutions. The prepared NCs were characterized using scanning electron microscopy, transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and thermogravimetric analysis. The results showed that MgO particles were successfully layered on GO. The impacts of different experimental variables on the removal of MB including GO/MgO NCs dosage, pH, contact time, and initial MB concentration were investigated.

Thereafter, we investigate the mechanism and kinetics of ozonation processes in the presence of GO/MgO NCs as a catalyst …

Evaluation Of The Mechanisms And Effectiveness Of Nano-Hydroxides, Wood And Dairy Manure-Derived Biochars To Remove Fluoride And Heavy Metals From Water, Anna Rose Wallace, Wenjie Sun Dr, Chunming Su Dr

Civil and Environmental Engineering Theses and Dissertations

The development of effective treatment processes for the removal contaminants, such as fluoride and heavy metals, from polluted water have been urgently needed due to serious environmental health and safety concerns. In this dissertation, a variety of materials including various (hydro)oxide nanomaterials, biochars and surface modified biochar were studied to evaluate their effectiveness and mechanism on removing fluoride or mixed heavy metals from water.

In the Chapter 2, this study investigated the adsorptive removal of fluoride from water using various (hydro)oxide nanomaterials, focusing on ferrihydrite, hydroxyapatite (HAP) and brucite, which have the potential to be used as sorbents for surface …

Failure Of High Strength Concrete Under Dynamic Uniaxial Compression, Colin Loeffler

Mechanical Engineering Research Theses and Dissertations

The failure strength of concrete materials has been widely shown to be dependent on experimental parameters such as specimen geometry and strain-rate. The effects of specimen geometry have been shown both theoretically and experimentally to be a result of the quasi-brittle nature of concrete. While the failure strength of concrete has been widely reported to increase significantly when deformed at high strain-rates, the physical mechanisms driving this phenomenon remain the source of debate amongst researchers. This means that constitutive models designed to predict this rate dependent behavior are not based on the real physical mechanisms that drive this behavior but …

Technology-Dependent Quantum Logic Synthesis And Compilation, Kaitlin Smith

Electrical Engineering Theses and Dissertations

The models and rules of quantum computation and quantum information processing (QIP) differ greatly from those that govern classical computation, and these differences have caused the implementation of quantum processing devices with a variety of new technologies. Many platforms have been developed in parallel, but at the time of writing, one method of quantum computing has not shown to be superior to the rest. Because of the variation that exists between quantum platforms, even between those of the same technology, there must be a way to automatically synthesize technology-independent quantum designs into forms that are capable of physical realization on …

Reducing The Production Cost Of Semiconductor Chips Using (Parallel And Concurrent) Testing And Real-Time Monitoring, Qutaiba Khasawneh

Electrical Engineering Theses and Dissertations

Consumer electronics changed the semiconductor industry by developing many new challenges for consumer products. One of the main challenges in the consumer product is that it propelled the volume of production to massive production, e.g. hundreds of millions of cell phones are produced yearly. Combined with the overproduction of consumer products, price pressure is another challenge for consumer products. Many of the new techniques used in the design and fabrication enabled the integration of more devices in the same chips. This reduced the cost of the chips, lowered the power consumption, increased the circuit operation speed, enabled more reliable implementation, …

Identifying Customer Churn In After-Market Operations Using Machine Learning Algorithms, Vitaly Briker, Richard Farrow, William Trevino, Brent Allen

SMU Data Science Review

This paper presents a comparative study on machine learning methods as they are applied to product associations, future purchase predictions, and predictions of customer churn in aftermarket operations. Association rules are used help to identify patterns across products and find correlations in customer purchase behaviour. Studying customer behaviour as it pertains to Recency, Frequency, and Monetary Value (RFM) helps inform customer segmentation and identifies customers with propensity to churn. Lastly, Flowserve’s customer purchase history enables the establishment of churn thresholds for each customer group and assists in constructing a model to predict future churners. The aim of this model is …

Resource Allocation And Task Scheduling Optimization In Cloud-Based Content Delivery Networks With Edge Computing, Yang Peng

Operations Research and Engineering Management Theses and Dissertations

The extensive growth in adoption of mobile devices pushes global Internet protocol (IP) traffic to grow and content delivery network (CDN) will carry 72 percent of total Internet traffic by 2022, up from 56 percent in 2017. In this praxis, Interconnected Cache Edge (ICE) based on different public cloud infrastructures with multiple edge computing sites is considered to help CDN service providers (SPs) to maximize their operational profit. The problem of resource allocation and performance optimization is studied in order to maximize the cache hit ratio with available CDN capacity.

The considered problem is formulated as a multi-stage stochastic linear …

Graphene-Based Water Desalination Using Atomistic Simulations, Thanh Chinh Nguyen

Mechanical Engineering Research Theses and Dissertations

My research focused on investigating saltwater transport through nanoporous graphene membranes using molecular dynamics (MD) simulations. Particularly, in this dissertation, we focused on pressure-driven flows of salt water through uncharged and charged nanoporous graphene membranes for water desalination applications. In the first study, desalination performance of uncharged single-layer nanoporous graphene membranes was observed based on volumetric flow rate, required pressure drop, and salt rejection efficiency. A functional relationship between the volumetric flow rate, pressure drop, pore diameter, and the dynamic viscosity of saltwater was also examined. In further studies, transport of salt ions through positively and negatively charged single-layer nanoporous …

Generalized Relay Network Design And Collaborative Dispatching In Truckload Transportation, Amin Ziaeifar

Operations Research and Engineering Management Theses and Dissertations

The truckload industry faces a serious problem of high driver shortage and turnover rate which is typically around 100\%. Among the major causes of this problem are extended on-the-road times where drivers handle several truckload pickup and deliveries successively; non-regular schedules and get-home rates; and low utilization of drivers dedicated time. These are by-and-large consequences of the driver-to-load dispatching method, which is based on point-to-point dispatching or direct shipment from origin-to-destination, commonly employed in the industry. In this dissertation, we consider an alternative dispatching method that necessitates careful design of an underlying network. In this scheme, a truckload on its …

Feedback Mechanisms For Centralized And Distributed Mobile Systems, Yan Shi

Electrical Engineering Theses and Dissertations

The wireless communication market is expected to witness considerable growth in the immediate future due to increasing smart device usage to access real-time data. Mobile devices become the predominant method of Internet access via cellular networks (4G/5G) and the onset of virtual reality (VR), ushering in the wide deployment of multiple bands, ranging from TVWhite Spaces to cellular/WiFi bands and on to mmWave. Multi-antenna techniques have been considered to be promising approaches in telecommunication to optimize the utilization of radio spectrum and minimize the cost of system construction. The performance of multiple antenna technology depends on the utilization of radio …

Development Of A Robotized Laser Directed Energy Deposition System And Process Challenges, Meysam Akbari

Mechanical Engineering Research Theses and Dissertations

Metal additive manufacturing (AM) is a disruptive technology, enabling fabrication of complex and near net shaped parts by adding material in a layer-wise fashion. It offers reduced lead production time, decreased buy-to-fly ratio, and repair and remanufacturing of high value components. AM processes are finding applications in many industrial sectors such as aerospace, automotive, biomedical and mold tooling. However, beside tremendous advantages of AM, there are still some challenges that prevent the adoption of this technology into high standard applications. Anisotropy and inhomogeneity in mechanical properties of the as-built parts and existence of pores and lack-of-fusion defects are considered as …

Big-Data Talent Analytics In The Public Sector: A Promotion And Firing Model Of Employees At Federal Agencies, Rabih Neouchi

Operations Research and Engineering Management Theses and Dissertations

Talent analytics is a relatively new area of focus to researchers working in analytics and data science. Talent Analytics has the potential to help companies make many informed critical decisions around talent acquisition, promotion and retention. This work investigates data science to predict “shiny star” employees in the U.S. public sector, defined as top-notch performers over the years of a given time span. Its scope falls within talent analytics, also called people analytics, a relatively new research area.

We clean a data set made available by the U.S. Office of Personnel Management (OPM) and present two models to predict the …

Leveraging Geographical And Spectral Information For Efficient Cellular Systems, Matthew Tonnemacher

Electrical Engineering Theses and Dissertations

With the unprecedented increase in mobile data demand and limited usable spectrum to provide for it, a paradigm shift towards spectrum sharing is a promising solution. However, there are many challenges that limit current spectrum sharing practices. One challenge is that proper spectrum sharing requires engaging devices to have an understanding of the impact they have on the ecosystem while transmitting in terms of spacial interference footprint and the implications to devices in their interference range. Another is that operators, especially licensed ones, have strict quality of service requirements for their subscribers, discouraging them from allowing unlicensed access of their …

Backhaul Profit Maximization Problem Instances, Yuanyuan Dong, Yulan Bai, Eli V. Olinick, Andrew Junfang Yu

Operations Research and Engineering Management

This archive contains data for the problem instances described in the technical report "An Empirical Study of Mixed Integer Programming Formulations of the Backhaul Profit Maximization Problem" by Yulan Bai and Eli V. Olinick.

An Empirical Study Of Mixed Integer Programming Formulations Of The Backhaul Profit Maximization Problem, Yulan Bai, Eli V. Olinick

Operations Research and Engineering Management

Solving an instance of the Backhaul Profit Maximization Problem (BPMP) requires simultaneously solving two problems: (1) determining how to route an empty delivery vehicle back from its current location to its depot by a scheduled arrival time, and (2) selecting a profit-maximizing subset of delivery requests between various locations on the route subject to the vehicle's capacity. We propose and test a series of enhancements to the node-arc and triples mixed integer programming formulations of BPMP found in the literature and develop a multi-criteria Composite Index Method (CIM) to evaluate the results. We find that CPLEX takes 5 to 34 …

Predicting Wind Turbine Blade Erosion Using Machine Learning, Casey Martinez, Festus Asare Yeboah, Scott Herford, Matt Brzezinski, Viswanath Puttagunta

SMU Data Science Review

Using time-series data and turbine blade inspection assessments, we present a classification model in order to predict remaining turbine blade life in wind turbines. Capturing the kinetic energy of wind requires complex mechanical systems, which require sophisticated maintenance and planning strategies. There are many traditional approaches to monitoring the internal gearbox and generator, but the condition of turbine blades can be difficult to measure and access. Accurate and cost- effective estimates of turbine blade life cycles will drive optimal investments in repairs and improve overall performance. These measures will drive down costs as well as provide cheap and clean electricity …

Machine Learning In Support Of Electric Distribution Asset Failure Prediction, Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, Jade Thiemsuwan

SMU Data Science Review

In this paper, we present novel approaches to predicting as- set failure in the electric distribution system. Failures in overhead power lines and their associated equipment in particular, pose significant finan- cial and environmental threats to electric utilities. Electric device failure furthermore poses a burden on customers and can pose serious risk to life and livelihood. Working with asset data acquired from an electric utility in Southern California, and incorporating environmental and geospatial data from around the region, we applied a Random Forest methodology to predict which overhead distribution lines are most vulnerable to fail- ure. Our results provide evidence …

Machine Learning To Predict The Likelihood Of A Personal Computer To Be Infected With Malware, Maryam Shahini, Ramin Farhanian, Marcus Ellis

SMU Data Science Review

In this paper, we present a new model to predict the prob- ability that a personal computer will become infected with malware. The dataset is selected from a Kaggle competition supported by Mi- crosoft. The data includes computer configuration, owner information, installed software, and configuration information. In our research, sev- eral classification models are utilized to assign a probability of a machine being infected with malware. The LightGBM classifier is the optimum machine learning model by performing faster with higher efficiency and lower memory usage in this research. The LightGBM algorithm obtained a cross-validation ROC-AUC score of 74%. Leading factors …

Aws Ec2 Instance Spot Price Forecasting Using Lstm Networks, Jeffrey Lancon, Yejur Kunwar, David Stroud, Monnie Mcgee, Robert Slater

SMU Data Science Review

Cloud computing is a network of remote computing resources hosted on the Internet that allow users to utilize cloud resources on demand. As such, it represents a paradigm shift in the way businesses and industries think about digital infrastructure. With the shift from IT resources being a capital expenditure to a managed service, companies must rethink how they approach utilizing and optimizing these resources in order to maximize productivity and minimize costs. With proper resource management, cloud resources can be instrumental in reducing computing expenses.

Cloud resources are perishable commodities; therefore, cloud service providers have developed strategies to maximize utilization …

Visualizing United States Energy Production Data, Bruce P. Kimbark, Melissa Luzardo, Charles South, James Taber

SMU Data Science Review

Power plants production, load, financials and environmental impact from power plants in the United States is publicly available either from the Energy Information Administration, the Environmental Protection Agency or Lazard among others. The general public is interested in US energy production and its potential environmental impact but the available information is complex and difficult to properly understand and not shared in ways that are accessible. Our objective was to gather this data and create different interactive visualizations that make it consumable. Each of the five visualization was designed to explain a specific part of energy that together can provide a …

Improve Image Classification Using Data Augmentation And Neural Networks, Shanqing Gu, Manisha Pednekar, Robert Slater

SMU Data Science Review

In this paper, we present how to improve image classification by using data augmentation and convolutional neural networks. Model overfitting and poor performance are common problems in applying neural network techniques. Approaches to bring intra-class differences down and retain sensitivity to the inter-class variations are important to maximize model accuracy and minimize the loss function. With CIFAR-10 public image dataset, the effects of model overfitting were monitored within different model architectures in combination of data augmentation and hyper-parameter tuning. The model performance was evaluated with train and test accuracy and loss, characteristics derived from the confusion matrices, and visualizations of …

Extreme-Point Tabu Search Heuristics For Fixed-Charge Generalized Network Problems, Angelika Leskovskaya

Operations Research and Engineering Management Theses and Dissertations

While researchers have studied generalized network flow problems extensively, the powerful addition of fixed charges on arcs has received scant attention. This work describes network-simplex-based algorithms that efficiently exploit the quasi-tree basis structure of the problem relaxations, proposes heuristics that utilize a candidate list, a tabu search with short and intermediate term memories to do the local search, a diversification approach to solve fixed-charge transportation problems, as well as a dynamic linearization of objective function extension for the transshipment fixed-charge generalized problems. Computational testings for both heuristics demonstrate their effectiveness in terms of speed and quality of solutions to these …

Investigation Of The Electrode Polarization Effect For Biosensor Applications, Anil Koklu

Mechanical Engineering Research Theses and Dissertations

My research focuses on electrokinetic transport. Particularly, in this dissertation, we focus on fabrication and testing of micro electrodes with nanostructured surfaces to minimize the electrode polarization (EP) effects for biosensor applications. In the first study, electrochemical deposition of gold nanoparticles on to planar gold electrodes was used to generate rough surfaces. Dendritic nanostructures that reduced EP up to two orders of magnitude was obtained by optimizing the deposition conditions. These structures also enhanced dielectrophoresis (DEP) response of our bio-chips, making them usable in physiological buffers. In further studies we discovered a universal scaling of EP in the frequency domain, …

Investigation Of Fundamental Principles Of Rigid Body Impact Mechanics, Khalid Alluhydan

Mechanical Engineering Research Theses and Dissertations

In impact mechanics, the collision between two or more bodies is a common, yet a very challenging problem. Producing analytical solutions that can predict the post-collision motion of the colliding bodies require consistent modeling of the dynamics of the colliding bodies. This dissertation presents a new method for solving the two and multibody impact problems that can be used to predict the post-collision motion of the colliding bodies. Also, we solve the rigid body collision problem of planar kinematic chains with multiple contacts with external surfaces.

In the first part of this dissertation, we study planar collisions of Balls and …

Impedance-Based Microfluidic Platform For Quantitative Biology, Amin Mansoorifar

Mechanical Engineering Research Theses and Dissertations

Dielectric properties of biological cells are functions of cellular structure, content, state, and phenotype. Dielectric spectroscopy (DS) is a nondestructive method to characterize dielectric properties by measuring impedance data over a frequency range. This method has been widely used for various applications such as counting, sizing, and monitoring biological cells and particles. Recently, this method has been suggested to be utilized in various stages of the drug discovery process due to its low sample consumption and fast analysis time.

In this thesis, we have developed a lab-on-a-chip device that uses an electro-activated microwells array for capturing, making DS measurements on, …

Long Term Software Quality And Reliability Assurance In A Small Company, Eric Abuta

Computer Science and Engineering Theses and Dissertations

Demonstrating software reliability across multiple software releases has become essential in making informed decisions of upgrading software releases without impacting significantly end users' characterized processes and software quality standards. Standard defect and workload data normally collected in a typical small software development organization can be used for this purpose. Objective of this study was to demonstrate how to measure software reliability in multiple releases and whether continuous defect fixes and code upgrades increased software reliability. This study looked at techniques such as trend test that evaluated software system's overall trend and stability, input domain reliability models (IDRM) that assessed system's …

Design And Control Of Fiber Encapsulation Additive Manufacturing, Matt Saari

Mechanical Engineering Research Theses and Dissertations

This work presents the design, development, and analysis of the Fiber Encapsulation Additive Manufacturing (FEAM) system developed at the Laboratory for Additive Manufacturing Robotics \& Automation at the Lyle School of Engineering at Southern Methodist University. The innovation introduced by FEAM is the ability to insert a continuous fiber of different material into the flowing extrudate. Correctly positioning the fiber feed inside the extrudate while turning the fiber in arbitrary directions is a critical aspect of the technology. This will allow for the full exploitation of the capabilities of the FEAM technology to produce robotic components that actuate and sense. …

Operation And Planning Of Data Centers In Electricity Networks, Ali Vafamehr

Electrical Engineering Theses and Dissertations

Cloud computing provides unique opportunities for the various sectors of the economy such as the automotive industry, education, finance, and governmental entities. In-house IT infrastructures vary in quality and can often be outdated and ineffective. As an alternative, cloud computing is a popular IT model which allows various businesses to “outsource” the IT infrastructure to a more efficient model. Businesses are opting to use cloud computing in order to eliminate the capital expenses of traditional IT systems and reduce the around-the-clock expert manpower required to operate on-site IT infrastructures. Relying on cloud services gives businesses the flexibility to access a …

Wireless Channel Characterization Based On Crowdsourced Data And Geographical Features, Rita Enami

Electrical Engineering Theses and Dissertations

To design and plan wireless communication systems, an accurate propagation estimate is required of a deployment region. Propagation prediction models consist of two types of fading: large-scale and small-scale fading. With large-scale fading, the path loss information is crucial for cell planning, coverage estimation, and optimization. With small-scale fading, the statistical fluctuation on the local variations of the average signal level can have a dramatic effect on protocol decisions and resulting performance. To obtain accurate estimates of both types of fading, typically field measurements are needed that use drive testing, which is expensive in terms of time and cost. Recently, …

Self-Driving Cars: Evaluation Of Deep Learning Techniques For Object Detection In Different Driving Conditions, Ramesh Simhambhatla, Kevin Okiah, Shravan Kuchkula, Robert Slater

SMU Data Science Review

Deep Learning has revolutionized Computer Vision, and it is the core technology behind capabilities of a self-driving car. Convolutional Neural Networks (CNNs) are at the heart of this deep learning revolution for improving the task of object detection. A number of successful object detection systems have been proposed in recent years that are based on CNNs. In this paper, an empirical evaluation of three recent meta-architectures: SSD (Single Shot multi-box Detector), R-CNN (Region-based CNN) and R-FCN (Region-based Fully Convolutional Networks) was conducted to measure how fast and accurate they are in identifying objects on the road, such as vehicles, pedestrians, …