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- Genre: Masters Thesis
Description
In a healthcare setting, the Sterile Processing Department (SPD) provides ancillary services to the Operating Room (OR), Emergency Room, Labor & Delivery, and off-site clinics. SPD's function is to reprocess reusable surgical instruments and return them to their home departments. The management of surgical instruments and medical devices can impact patient safety and hospital revenue. Any time instrumentation or devices are not available or are not fit for use, patient safety and revenue can be negatively impacted. One step of the instrument reprocessing cycle is sterilization. Steam sterilization is the sterilization method used for the majority of surgical instruments and is preferred to immediate use steam sterilization (IUSS) because terminally sterilized items can be stored until needed. IUSS Items must be used promptly and cannot be stored for later use. IUSS is intended for emergency situations and not as regular course of action. Unfortunately, IUSS is used to compensate for inadequate inventory levels, scheduling conflicts, and miscommunications. If IUSS is viewed as an adverse event, then monitoring IUSS incidences can help healthcare organizations meet patient safety goals and financial goals along with aiding in process improvement efforts. This work recommends statistical process control methods to IUSS incidents and illustrates the use of control charts for IUSS occurrences through a case study and analysis of the control charts for data from a health care provider. Furthermore, this work considers the application of data mining methods to IUSS occurrences and presents a representative example of data mining to the IUSS occurrences. This extends the application of statistical process control and data mining in healthcare applications.
ContributorsWeart, Gail (Author) / Runger, George C. (Thesis advisor) / Li, Jing (Committee member) / Shunk, Dan (Committee member) / Arizona State University (Publisher)
Created2014
Description
There has been considerable advancement in the algae research field to move algae production for biofuels and bio-products forward to become commercially viable. However, there is one key element that humans cannot control, the natural externalities that impact production. An algae cultivation system is similar to agricultural crop farming practices. Algae are grown on an area of land for a certain time period with the aim of harvesting the biomass produced. One of the advantages of using algae biomass is that it can be used as a source of energy in the form of biofuels. Major advances in algae research and development practices have led to new knowledge about the remarkable potential of algae to serve as a sustainable source of biofuel. The challenge is to make the price of biofuels from algae cost-competitive with the price of petroleum-based fuels. The scope of this research was to design a concept for an automated system to control specific externalities and determine if integrating the system in an algae cultivation system could improve the algae biomass production process. This research required the installation and evaluation of an algae cultivation process, components selection and computer software programming for an automated system. The results from the automated system based on continuous real time monitored variables validated that the developed system contributes insights otherwise not detected from a manual measurement approach. The implications of this research may lead to technology that can be used as a base model to further improve algae cultivation systems.
ContributorsPuruhito, Emil (Author) / Sommerfeld, Milton (Thesis advisor) / Gintz, Jerry (Thesis advisor) / Alford, Eddie (Committee member) / Arizona State University (Publisher)
Created2014
Description
Network traffic analysis by means of Quality of Service (QoS) is a popular research and development area among researchers for a long time. It is becoming even more relevant recently due to ever increasing use of the Internet and other public and private communication networks. Fast and precise QoS analysis is a vital task in mission-critical communication networks (MCCNs), where providing a certain level of QoS is essential for national security, safety or economic vitality. In this thesis, the details of all aspects of a comprehensive computational framework for QoS analysis in MCCNs are provided. There are three main QoS analysis tasks in MCCNs; QoS measurement, QoS visualization and QoS prediction. Definitions of these tasks are provided and for each of those, complete solutions are suggested either by referring to an existing work or providing novel methods.
A scalable and accurate passive one-way QoS measurement algorithm is proposed. It is shown that accurate QoS measurements are possible using network flow data.
Requirements of a good QoS visualization platform are listed. Implementations of the capabilities of a complete visualization platform are presented.
Steps of QoS prediction task in MCCNs are defined. The details of feature selection, class balancing through sampling and assessing classification algorithms for this task are outlined. Moreover, a novel tree based logistic regression method for knowledge discovery is introduced. Developed prediction framework is capable of making very accurate packet level QoS predictions and giving valuable insights to network administrators.
A scalable and accurate passive one-way QoS measurement algorithm is proposed. It is shown that accurate QoS measurements are possible using network flow data.
Requirements of a good QoS visualization platform are listed. Implementations of the capabilities of a complete visualization platform are presented.
Steps of QoS prediction task in MCCNs are defined. The details of feature selection, class balancing through sampling and assessing classification algorithms for this task are outlined. Moreover, a novel tree based logistic regression method for knowledge discovery is introduced. Developed prediction framework is capable of making very accurate packet level QoS predictions and giving valuable insights to network administrators.
ContributorsSenturk, Muhammet Burhan (Author) / Li, Jing (Thesis advisor) / Baydogan, Mustafa G (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2014
Description
Microalgae represent a potential sustainable alternative for the enhancement and protection of agricultural crops. The dry biomass and cellular extracts of Scenedesmus dimorphus were applied as a biofertilizer, a foliar spray, and a seed primer to evaluate seed germination, plant growth, and crop yield of Roma tomato plants. The dry biomass was applied as a biofertilizer at 50 g and 100 g per plant, to evaluate its effects on plant development and crop yield. Biofertilizer treatments enhanced plant growth and led to greater crop (fruit) production. Timing of biofertilizer application proved to be of importance - earlier 50 g biofertilizer application resulted in greater plant growth. Scenedesmus dimorphus culture, growth medium, and different concentrations (1%, 5%, 10%, 25%, 50%, 75%, 100%) of aqueous cell extracts were used as seed primers to determine effects on germination. Seeds treated with Scenedesmus dimorphus culture and with extract concentrations higher than 50 % (0.75 g ml-1) triggered faster germination - 2 days earlier than the control group. Extract foliar sprays of 50 ml and 100 ml, were obtained and applied to tomato plants at various extract concentrations (10%, 25%, 50%, 75% and 100%). Plant height, flower development and number of branches were significantly enhanced with 50 % (7.5 g ml-1) extracts. Higher concentration sprays led to a decrease in growth. The extracts were further screened to assess potential antimicrobial activity against the bacterium Escherichia coli ATCC 25922, the fungi Candida albicans ATCC 90028 and Aspergillus brasiliensis ATCC 16404. No antimicrobial activity was observed from the microalga extracts on the selected microorganisms.
ContributorsGarcia-Gonzalez, Jesus (Author) / Sommerfeld, Milton (Thesis advisor) / Steele, Kelly (Committee member) / Henderson, Mark (Committee member) / Arizona State University (Publisher)
Created2014
Description
Second-generation biofuel feedstocks are currently grown in land-based systems that use valuable resources like water, electricity and fertilizer. This study investigates the potential of near-shore marine (ocean) seawater filtration as a source of planktonic biomass for biofuel production. Mixed marine organisms in the size range of 20µm to 500µm were isolated from the University of California, Santa Barbara (UCSB) seawater filtration system during weekly backwash events between the months of April and August, 2011. The quantity of organic material produced was determined by sample combustion and calculation of ash-free dry weights. Qualitative investigation required density gradient separation with the heavy liquid sodium metatungstate followed by direct transesterification and gas chromatography with mass spectrometry (GC-MS) of the fatty acid methyl esters (FAME) produced. A maximum of 0.083g/L of dried organic material was produced in a single backwash event and a study average of 0.036g/L was calculated. This equates to an average weekly value of 7,674.75g of dried organic material produced from the filtration of approximately 24,417,792 liters of seawater. Temporal variations were limited. Organic quantities decreased over the course of the study. Bio-fouling effects from mussel overgrowth inexplicably increased production values when compared to un-fouled seawater supply lines. FAMEs (biodiesel) averaged 0.004% of the dried organic material with 0.36ml of biodiesel produced per week, on average. C16:0 and C22:6n3 fatty acids comprised the majority of the fatty acids in the samples. Saturated fatty acids made up 30.71% to 44.09% and unsaturated forms comprised 55.90% to 66.32% of the total chemical composition. Both quantities and qualities of organics and FAMEs were unrealistic for use as biodiesel but sample size limitations, system design, geographic and temporal factors may have impacted study results.
ContributorsPierre, Christophe (Author) / Olson, Larry (Thesis advisor) / Sommerfeld, Milton (Committee member) / Brown, Albert (Committee member) / Arizona State University (Publisher)
Created2011
Description
In 2010, a monthly sampling regimen was established to examine ecological differences in Saguaro Lake and Lake Pleasant, two Central Arizona reservoirs. Lake Pleasant is relatively deep and clear, while Saguaro Lake is relatively shallow and turbid. Preliminary results indicated that phytoplankton biomass was greater by an order of magnitude in Saguaro Lake, and that community structure differed. The purpose of this investigation was to determine why the reservoirs are different, and focused on physical characteristics of the water column, nutrient concentration, community structure of phytoplankton and zooplankton, and trophic cascades induced by fish populations. I formulated the following hypotheses: 1) Top-down control varies between the two reservoirs. The presence of piscivore fish in Lake Pleasant results in high grazer and low primary producer biomass through trophic cascades. Conversely, Saguaro Lake is controlled from the bottom-up. This hypothesis was tested through monthly analysis of zooplankton and phytoplankton communities in each reservoir. Analyses of the nutritional value of phytoplankton and DNA based molecular prey preference of zooplankton provided insight on trophic interactions between phytoplankton and zooplankton. Data from the Arizona Game and Fish Department (AZGFD) provided information on the fish communities of the two reservoirs. 2) Nutrient loads differ for each reservoir. Greater nutrient concentrations yield greater primary producer biomass; I hypothesize that Saguaro Lake is more eutrophic, while Lake Pleasant is more oligotrophic. Lake Pleasant had a larger zooplankton abundance and biomass, a larger piscivore fish community, and smaller phytoplankton abundance compared to Saguaro Lake. Thus, I conclude that Lake Pleasant was controlled top-down by the large piscivore fish population and Saguaro Lake was controlled from the bottom-up by the nutrient load in the reservoir. Hypothesis 2 stated that Saguaro Lake contains more nutrients than Lake Pleasant. However, Lake Pleasant had higher concentrations of dissolved nitrogen and phosphorus than Saguaro Lake. Additionally, an extended period of low dissolved N:P ratios in Saguaro Lake indicated N limitation, favoring dominance of N-fixing filamentous cyanobacteria in the phytoplankton community in that reservoir.
ContributorsSawyer, Tyler R (Author) / Neuer, Susanne (Thesis advisor) / Childers, Daniel L. (Committee member) / Sommerfeld, Milton (Committee member) / Arizona State University (Publisher)
Created2011
Description
Modern, advanced statistical tools from data mining and machine learning have become commonplace in molecular biology in large part because of the “big data” demands of various kinds of “-omics” (e.g., genomics, transcriptomics, metabolomics, etc.). However, in other fields of biology where empirical data sets are conventionally smaller, more traditional statistical methods of inference are still very effective and widely used. Nevertheless, with the decrease in cost of high-performance computing, these fields are starting to employ simulation models to generate insights into questions that have been elusive in the laboratory and field. Although these computational models allow for exquisite control over large numbers of parameters, they also generate data at a qualitatively different scale than most experts in these fields are accustomed to. Thus, more sophisticated methods from big-data statistics have an opportunity to better facilitate the often-forgotten area of bioinformatics that might be called “in-silicomics”.
As a case study, this thesis develops methods for the analysis of large amounts of data generated from a simulated ecosystem designed to understand how mammalian biomechanics interact with environmental complexity to modulate the outcomes of predator–prey interactions. These simulations investigate how other biomechanical parameters relating to the agility of animals in predator–prey pairs are better predictors of pursuit outcomes. Traditional modelling techniques such as forward, backward, and stepwise variable selection are initially used to study these data, but the number of parameters and potentially relevant interaction effects render these methods impractical. Consequently, new modelling techniques such as LASSO regularization are used and compared to the traditional techniques in terms of accuracy and computational complexity. Finally, the splitting rules and instances in the leaves of classification trees provide the basis for future simulation with an economical number of additional runs. In general, this thesis shows the increased utility of these sophisticated statistical techniques with simulated ecological data compared to the approaches traditionally used in these fields. These techniques combined with methods from industrial Design of Experiments will help ecologists extract novel insights from simulations that combine habitat complexity, population structure, and biomechanics.
As a case study, this thesis develops methods for the analysis of large amounts of data generated from a simulated ecosystem designed to understand how mammalian biomechanics interact with environmental complexity to modulate the outcomes of predator–prey interactions. These simulations investigate how other biomechanical parameters relating to the agility of animals in predator–prey pairs are better predictors of pursuit outcomes. Traditional modelling techniques such as forward, backward, and stepwise variable selection are initially used to study these data, but the number of parameters and potentially relevant interaction effects render these methods impractical. Consequently, new modelling techniques such as LASSO regularization are used and compared to the traditional techniques in terms of accuracy and computational complexity. Finally, the splitting rules and instances in the leaves of classification trees provide the basis for future simulation with an economical number of additional runs. In general, this thesis shows the increased utility of these sophisticated statistical techniques with simulated ecological data compared to the approaches traditionally used in these fields. These techniques combined with methods from industrial Design of Experiments will help ecologists extract novel insights from simulations that combine habitat complexity, population structure, and biomechanics.
ContributorsSeto, Christian (Author) / Pavlic, Theodore (Thesis advisor) / Li, Jing (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2018
Description
Mathematical modeling and decision-making within the healthcare industry have given means to quantitatively evaluate the impact of decisions into diagnosis, screening, and treatment of diseases. In this work, we look into a specific, yet very important disease, the Alzheimer. In the United States, Alzheimer’s Disease (AD) is the 6th leading cause of death. Diagnosis of AD cannot be confidently confirmed until after death. This has prompted the importance of early diagnosis of AD, based upon symptoms of cognitive decline. A symptom of early cognitive decline and indicator of AD is Mild Cognitive Impairment (MCI). In addition to this qualitative test, Biomarker tests have been proposed in the medical field including p-Tau, FDG-PET, and hippocampal. These tests can be administered to patients as early detectors of AD thus improving patients’ life quality and potentially reducing the costs of the health structure. Preliminary work has been conducted in the development of a Sequential Tree Based Classifier (STC), which helps medical providers predict if a patient will contract AD or not, by sequentially testing these biomarker tests. The STC model, however, has its limitations and the need for a more complex, robust model is needed. In fact, STC assumes a general linear model as the status of the patient based upon the tests results. We take a simulation perspective and try to define a more complex model that represents the patient evolution in time.
Specifically, this thesis focuses on the formulation of a Markov Chain model that is complex and robust. This Markov Chain model emulates the evolution of MCI patients based upon doctor visits and the sequential administration of biomarker tests. Data provided to create this Markov Chain model were collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The data lacked detailed information of the sequential administration of the biomarker tests and therefore, different analytical approaches were tried and conducted in order to calibrate the model. The resulting Markov Chain model provided the capability to conduct experiments regarding different parameters of the Markov Chain and yielded different results of patients that contracted AD and those that did not, leading to important insights into effect of thresholds and sequence on patient prediction capability as well as health costs reduction.
The data in this thesis was provided from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). ADNI investigators did not contribute to any analysis or writing of this thesis. A list of the ADNI investigators can be found at: http://adni.loni.usc.edu/about/governance/principal-investigators/ .
Specifically, this thesis focuses on the formulation of a Markov Chain model that is complex and robust. This Markov Chain model emulates the evolution of MCI patients based upon doctor visits and the sequential administration of biomarker tests. Data provided to create this Markov Chain model were collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The data lacked detailed information of the sequential administration of the biomarker tests and therefore, different analytical approaches were tried and conducted in order to calibrate the model. The resulting Markov Chain model provided the capability to conduct experiments regarding different parameters of the Markov Chain and yielded different results of patients that contracted AD and those that did not, leading to important insights into effect of thresholds and sequence on patient prediction capability as well as health costs reduction.
The data in this thesis was provided from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). ADNI investigators did not contribute to any analysis or writing of this thesis. A list of the ADNI investigators can be found at: http://adni.loni.usc.edu/about/governance/principal-investigators/ .
ContributorsCamarena, Raquel (Author) / Pedrielli, Giulia (Thesis advisor) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2018
Description
Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high dimensionality and complex spatial structures. Recent advancement of the unsupervised deep models such as a generative adversarial network (GAN) and generative adversarial autoencoder (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational autoencoder, which serves as a nonlinear dimension reduction technique with regularization from the discriminator. Based on this, we propose a monitoring statistic efficiently capturing the change of the image data. The performance of the proposed AAE-based anomaly detection algorithm is validated through a simulation study and real case study for rolling defect detection.
ContributorsYeh, Huai-Ming (Author) / Yan, Hao (Thesis advisor) / Pan, Rong (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2019
Description
Hepatocellular carcinoma (HCC) is a malignant tumor and seventh most common cancer in human. Every year there is a significant rise in the number of patients suffering from HCC. Most clinical research has focused on HCC early detection so that there are high chances of patient's survival. Emerging advancements in functional and structural imaging techniques have provided the ability to detect microscopic changes in tumor micro environment and micro structure. The prime focus of this thesis is to validate the applicability of advanced imaging modality, Magnetic Resonance Elastography (MRE), for HCC diagnosis. The research was carried out on three HCC patient's data and three sets of experiments were conducted. The main focus was on quantitative aspect of MRE in conjunction with Texture Analysis, an advanced imaging processing pipeline and multi-variate analysis machine learning method for accurate HCC diagnosis. We analyzed the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Along with this we studied different machine learning algorithms and developed models using them. Performance metrics such as Prediction Accuracy, Sensitivity and Specificity have been used for evaluation for the final developed model. We were able to identify the significant features in the dataset and also the selected classifier was robust in predicting the response class variable with high accuracy.
ContributorsBansal, Gaurav (Author) / Wu, Teresa (Thesis advisor) / Mitchell, Ross (Thesis advisor) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2013