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Title: Functionalized 2D Materials as Enablers of High Energy and High Power Energy Storage Devices
Creator: Radhakrishnan, Sivaviswa
Date: 2023
Description: The present Thesis concerns with the synthesis of novel functionalized 2D materials for applications as cathodes in lithium-ion batteries. It...
Show moreThe present Thesis concerns with the synthesis of novel functionalized 2D materials for applications as cathodes in lithium-ion batteries. It further concerns with the role of porosity in these novel cathode materials to achieve simultaneously high energy and power density. Examples of the novel cathode materials synthesized here include several functionalized hexagonal boron nitride (hBN) and graphene (G) species. hBN was functionalized with Li₂C₂O₄, LiBF₄, -OBF₂ groups, NOBF₄, etc. The color of the functionalized hBN species ranges from white through brown to black indicating drastic changes in the band structure of hBN due to functionalization. Functionalized G species include Li₂C₂O₄ and -OBF₃ functionalized ones. Preliminary electrochemical tests were carried out for an initial assessment of the properties of these materials. Additionally, the role of the DOL solvent was also investigated in high power CFx batteries
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Title: Design and Fabrication of Battery-Operated Radiator Control (BORC) Utilizing 3D Printing Strategies
Creator: Riley, Christopher W.
Date: 2023
Description: The scope of this work aims to serve as a continuation of prior research focused on the “development and evaluation of an automatic steam...
Show moreThe scope of this work aims to serve as a continuation of prior research focused on the “development and evaluation of an automatic steam radiator control system or retrofitting legacy heating systems in existing buildings” (Syed Ali et al., 2020) by describing and testing the mechanical components of the developed controller in full detail. Other aspects of radiator efficiency are also explored. Primarily, this work aims to elaborate on the importance of material selection and mechanical properties of the design process. It also proposes initiative-taking solutions for the building’s energy recovery by monitoring the initial set up and focusing on certain details such as cardinal direction, thermal breaks, etc. These legacy systems are generally problematic when attempting to calculate energy efficiency, as a majority of radiator controls tend to be manual. Though there are comparable products within the European market, they cater to hot water systems and not steam, and in some instances require an internet bridge for operation (Tahersima et al., 2010). Since this is an extension of our earlier project, I will refer to it as Battery Operated Radiator Control (BORC) and the previous version as BERG’s Automated Radiator Control (ARC).
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Title: The Effect of Time Step on HSPF Model Performance
Creator: Rubinstein, Benjamin J.
Date: 2023
Description: Hydrological modeling is a mature and well researched field; however, because most climate data are collected on hour or greater time...
Show moreHydrological modeling is a mature and well researched field; however, because most climate data are collected on hour or greater time intervals there is very little research on the effect of using high resolution data as inputs for the models. A Python tool for downloading high resolution five minute interval data from the Oklahoma Mesonet was created and the PyHSPF Python package was used to generate, calibrate, and validate HSPF models using five minute, one hour, and daily time steps. Flow errors, R², and Nash-Sutcliffe efficiency for simulated outflows, and resource usage were compared for each model. The hourly and five minute models performed similarly well, and the daily model performed significantly worse. The results of this work could prove useful for policy makers and researchers looking to update or create new climate data collection protocols, and the tools used can be applied to many different kinds of future research.
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Title: Understanding Location Bias in Fake News Datasets of Twitter
Creator: Patil, Kayenat Kailas
Date: 2023
Description: Fake news tends to spread faster and wider than real news. It has a greater impact and can lead to negative and dangerous outcomes. With the...
Show moreFake news tends to spread faster and wider than real news. It has a greater impact and can lead to negative and dangerous outcomes. With the world spending an increasing amount of time on their mobile devices, people tend to get more of their news from their desired social media platform. It has become part of our daily lives, whether it is to keep in touch with friends and family, to getting gossip on celebrities or even shopping. In 2022, the average time a person spends per day on the internet on a social media platform has been accounted to be about 147 minutes,[1] indicating an increase in time spent scrolling through information online.It has become a widespread phenomenon in recent years, thanks in part to the rapid spread of information through social media and other online channels. It is increasingly important to explore and understand fake news and its impact on society, as well as to develop effective tools and methods for detecting and combating it. There are several factors that can tamper with the successful detection of fake news. Machine learning models often fall to such biases that result in inaccurate predictions. There are several biases that have been identified like age, gender, sex and many more. In this thesis, we are exploring location as a form of a bias and if it hinders prediction. We have looked at location from two perspectives. One, taking location as co-ordinates in the form of latitude and longitude and analyzing the likelihood of a tweet coming from a location to be fake or not. The second method we have used is that we have considered location as an entity and used natural language processing model to see if its able to predict if the given tweet is fake or not, along with masking the location mentioned in the tweet and analyzing how the performance of the model changes. Machine learning models can play an important role in fake news detection models, by analyzing large amounts of data and identifying patterns and indicators that suggest a piece of information may be false or misleading, but they are often susceptible to some form of biases. By studying biases on machine learning models on fake news datasets, we can develop more effective tools for identifying fake news and taking steps towards mitigating it, ultimately helping to protect the integrity of information and promote informed decision-making in society.
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Title: Effect of Pre-Processing Data on Fairness and Fairness Debugging using GOPHER
Creator: Sarkar, Mousam
Date: 2023
Description: At present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed...
Show moreAt present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed throughout and present studies’ direct usage of eXplainable Artificial Intelligence (XAI) approaches to identify and study bias. To solve the problem of locating bias and then mitigating it has been achieved by Gopher [1]. It generates interpretable top-k explanations for the unfairness of the model and it also identifies subsets of training data that are the root cause of this unfair behavior. We utilize this system to study the effect of pre-processing on bias through provenance. The concept of data lineage through tagging of data points during and after the pre-processing stage is implemented. Our methodology and results provide a useful point of reference for studying the relation of pre-processing data with the unfairness of the machine learning model.
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Title: Establishing Bisphenol A Degradation and Enhancing Microbial Fuel Cell Performance by Biofilm Optimization of Shewanella Oneidensis MR1
Creator: Zhou, Jiacheng
Date: 2023
Description: Bisphenol A (BPA) has been widely used as a plasticizer in the production of synthetic polymers, such as those used in food storage containers...
Show moreBisphenol A (BPA) has been widely used as a plasticizer in the production of synthetic polymers, such as those used in food storage containers and bottles. However, BPA interferes with endocrine systems, causing carcinogenicity, immunotoxicity, and embryotoxicity. Biological water treatment processes scarcely remove BPA, owing to the poor BPA degradability and efficiency of the applied microorganisms. Shewanella oneidensis has been studied and used for the biodegradation process in wastewater treatment because of its excellent extracellular electron transfer properties. In this work, we engineered S. oneidensis MR1 to enable BPA degradation by producing ferredoxin (Fdbisd) and cytochrome P450 (P450bisd) originating from Sphingomonas bisphenolicum AO1. The engineered S. oneidensis exhibited a higher BPA degradation efficiency than that of Escherichia coli producing the same enzymes. The endogenous ferredoxin and ferredoxin reductase of S. oneidensis participated in BPA degradation, and overexpression of mtrC, omcA, and So0521, which encode S. oneidensis cytochromes, decreased BPA. We developed BPA-degrading S. oneidensis biofilms. We measured these optimized BPA-degrading S. oneidensis biofilm in a single chamber microbial fuel cell formed on different carbon electrodes by morphology. Cyclic voltammetry and electrochemical impedance spectroscopy were measured to analyze the biofilm-electrode performance. The biofilm colonization was also measured by confocal laser scanning microscope and scanning electron microscope. And the developed microbial fuel cell was used to degrade BPA and the biofilm developed on different type of carbon anodes was identified. This study provides insights into biocatalyst utilization for the biological degradation of toxic organic compounds.
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Title: Machine Learning for NDE Imaging Applications
Creator: Zhang, Xin
Date: 2023
Description: Infrared Thermography and Ultrasonic Imaging of materials are promising non-destructive evaluation (NDE) methods but signals face challenges...
Show moreInfrared Thermography and Ultrasonic Imaging of materials are promising non-destructive evaluation (NDE) methods but signals face challenges to be analyzed and characterized due to the nature of complex signal patterns and poor signal-to-noise ratios (SNR). Industries such as nuclear energy, are constructed with components produced using high-strength superalloys. These metallic components face challenges for wide deployment because material defects and mechanical conditions need to be non-destructively evaluated to identify potential danger before they enter service. Low NDE performance and lack of automation, particularly considering the complex environment in the in-situation NDE and nuclear power plant, present a major challenge to implement conventional NDE. This study solves the problems of using the advantages of machine learning as signal processing methods for Infrared Thermography and Ultrasonic NDE imaging applications. In Pulsed Infrared Thermography (PIT), for quality control of metal additive manufacturing, we proposed an intelligent PIT NDE system and developed innovative unsupervised learning models and thermal tomography 3D imaging algorithms to detect calibrated internal defects (pores) of various sizes and depths for different nuclear-grade metallic structures. Unsupervised learning aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Difficulties to detect defects by using PIT are thermal imaging noise patterns; uneven heating of the specimen; defects of micron-level size with overly weak temperature signals and so on. The unsupervised learning methods overcome these barriers and achieve the high defect detection accuracies (F-score) of 0.96 to detect large defects and 0.89 to detect microscopic defects, and can successfully detect defects with diameter of only 0.101-mm. In addition, we researched and developed innovative unsupervised learning models to compress high-resolution PIT imaging data and achieve the average high compression ratio >30 and a highest compression of 46 with reconstruction accuracy peak signal-to-noise ratio (PSNR) >73dB while preserving weak thermal features corresponding to microscopic defects. In ultrasonic NDE imaging, for structural health monitoring of materials, we built a high-performance ultrasonic computational system to inspect the integrity of high-strength metallic materials which are used in high-temperature corrosive environments of nuclear reactors. For system automation, we have been developing neural networks with various architectures for grain size estimation by characterizing the ultrasonic backscattered signals with high accuracy and data-efficiency. In addition, we introduce a response-based teacher-student knowledge distillation training framework to train neural networks and achieve 99.27% characterization accuracy with a high image processed throughput of 192 images/second on testing. Furthermore, we introduce a reinforcement learning based neural architecture search framework to automatically model the optimal neural networks design for ultrasonic flaws detection. At last, we comprehensively researched the performance of using unsupervised learning methods to compress 3D ultrasonic data and achieve high compression performance using only 4.25% of the acquired experimental data.
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Title: Image Synthesis with Generative Adversarial Networks
Creator: Ouyang, Xu
Date: 2023
Description: Image synthesis refers to the process of generating new images from an existing dataset, with the objective of creating images that closely...
Show moreImage synthesis refers to the process of generating new images from an existing dataset, with the objective of creating images that closely resemble the target images, learned from the source data distribution. This technique has a wide range of applications, including transforming captions into images, deblurring blurred images, and enhancing low-resolution images. In recent years, deep learning techniques, particularly Generative Adversarial Network (GAN), has achieved significant success in this field. GAN consists of a generator (G) and a discriminator (D) and employ adversarial learning to synthesize images. Researchers have developed various strategies to improve GAN performance, such as controlling learning rates for different models and modifying the loss functions. This thesis focuses on image synthesis from captions using GANs and aims to improve the quality of generated images. The study is divided into four main parts:In the first part, we investigate the LSTM conditional GAN which is to generate images from captions. We use the word2vec as the caption features and combine these features’ information by LSTM and generate images via conditional GAN. In the second part, to improve the quality of generated images, we address the issue of convergence speed and enhance GAN performance using an adaptive WGAN update strategy. We demonstrate that this update strategy is applicable to Wasserstein GAN(WGAN) and other GANs that utilize WGAN-related loss functions. The proposed update strategy is based on a loss change ratio comparison between G and D. In the third part, to further enhance the quality of synthesized images, we investigate a transformer-based Uformer GAN for image restoration and propose a two-step refinement strategy. Initially, we train a Uformer model until convergence, followed by training a Uformer GAN using the restoration results obtained from the first step.In the fourth part, to generate fine-grained image from captions, we delve into the Recurrent Affine Transformation (RAT) GAN for fine-grained text-to-image synthesis. By incorporating an auxiliary classifier in the discriminator and employing a contrastive learning method, we improve the accuracy and fine-grained details of the synthesized images.Throughout this thesis, we strive to enhance the capabilities of GANs in various image synthesis applications and contribute valuable insights to the field of deep learning and image processing.
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Title: Online Satire in Iran: Social Critique, Counter-Publicity and Public Opinion
Creator: Mirghaderi, Leilasadat
Date: 2023
Description: This dissertation examines and investigates the recontextualization of theories of satire, public sphere, and communicative action in the...
Show moreThis dissertation examines and investigates the recontextualization of theories of satire, public sphere, and communicative action in the context of Iranian Instagram satirists. The importance of this investigation is two-fold. First, since the advent of social media sites, several theoretical concepts have gone through paradigm shifts. In this regard, theories of satire, public sphere, and communicative action are no exceptions. Second, these theoretical concepts were primarily developed and analyzed in Western democratic nations and need to be revisited in the context of Iran. To this end, by looking through the lenses of these theories and taking into account the context of Iran, this dissertation identifies the major topics that Iranian Instagram satirists address and criticize. Therefore, the first goal of this dissertation is to provide insights with regards to how Iranian Instagram satirists are using humor to criticize different aspects of social, cultural, and political matters that are more salient in the context of Iran and to investigate how they are trying to transfer their knowledge and raise awareness about topics that are less publicly discussed.Furthermore, by analyzing the topics that are the main focus of Iranian Instagram satirists, accompanied by observations from my netnography study, and considering the current atmosphere and historical background of Iran, this dissertation argues the extent to which Iranian satirical content on Instagram facilitates the democratic exchange of ideas as a requirement for transformation from a public space to public sphere(s). This dissertation also examines the communicative action strategies that Iranian Instagram satirists employ to reach an understanding with the state as well as their followers. Finally, the dimensions and attributes of satire as well as the features of Instagram that facilitate the formation of public discourse and public opinion formation in Iran are identified. Methodologically, this dissertation utilized a combination of qualitative research methods including but not limited to netnography, thematic and content analysis, transcription of publicly available trade publication interviews, and critical discourse analysis. While themes and topics were extracted from the collected media from the Instagram pages of the Iranian Instagram satirists, the transcription of the publicly available trade publication interviews as well as critical discourse analysis were used to describe why some topics were more salient and to provide answers to the developed research questions. From the analysis of the posts and stories, ten themes that fall into two broad categories of inward and outward criticism were extracted. In the context of this research, the inward criticism category refers to the topics that unravel the social and cultural dimensions of relationships and interactions, while the outward criticism category refers to the topics that criticize the state and its established institutions. Taking a closer look at the results, we can see that in the context of Iran, it can be concluded that the online world accompanied by satire facilitates the democratic exchange of ideas as a requirement for transformation from a public space to public sphere(s). However, as Iranian Instagram influencers are expressing their opposing thoughts against the established hegemony and are also challenging what matters belong to the private or public domains, it can be said that they are forming subaltern counterpublics in the online world.
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Title: Modeling and Control of Boundary Constrained Granular Swarm Robots
Creator: Mulroy, Declan Augustine
Date: 2023
Description: Soft robots offer many advantages that traditional robotic systems do not. Soft robotic systems are able to safely interact with their...
Show moreSoft robots offer many advantages that traditional robotic systems do not. Soft robotic systems are able to safely interact with their environment and tolerate large deformations. This is due to being composed of soft materials, which allows them to be subjected to and experience large deformations. However, they still have limitations in their maneuverability, locomotion, and force exertion. Moreover, they usually require external tethering or other specialized systems, such as pneumatic devices, to function. To address some of these limitations, a novel class of robotic systems has emerged called a boundary-constrained granular swarm robot.A boundary-constrained granular swarm robot is composed of a closed-loop series of active sub-robots, each with the ability to locomote. Each sub-robot is connected to its neighbors with an elastic membrane, which forms a single robot. The membrane encloses a passive granular interior, which provides structure and allows the robot to switch between rigid and soft states via granular jamming phase transitions. This allows for the robotic system to exploit the desirable characteristics of both soft and rigid robots. However, there is limited research with regards to modeling and controlling this system due to its novelty. This thesis addresses this gap by presenting several simulation frameworks, which incorporates multi-body dynamics and non-smooth contact dynamics to model the forward dynamics of the system. These models are able to account for the frictional effects, and the contact forces experienced by the system. The developed models are verified through experimental prototypes to ensure the models are able to capture the general behaviors of the system. Additionally, gradient-based control algorithms are presented and applied to simulated and experimental systems to have each of them form arbitrary shapes, morph between shapes, grasp arbitrarily shaped objects, and navigate narrow corridors. All of these objectives have been accomplished in previous systems, however, this thesis will demonstrate this system is one of the first to be able to accomplish all four. Moreover, it is able to by using a single control framework. In addition, this thesis will present the application distance functions, R-functions, and space-time transfinite interpolation for control purposes. These techniques are commonly utilized in graphics and animation theory, and will be applied to gradient-based controllers. These controllers will be used for boundary constrained granular swarms to form desired shapes and morph between shapes in both 2D and 3D simulated systems and experimental systems. Moreover, this thesis will explore the use of grasping metrics for boundary-constrained granular swarms. The Ferrari Canny metric, a well-established tool for assessing grasp quality in robotic manipulators, is utilized to evaluate the system’s grasp performance. This thesis will also demonstrate the application of this metric for boundary-constrained swarms to find the optimal angle of approach for the system to grasp a target object.
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Title: Understanding the Impact of Age and Gender Demographic Similarity in Assessment Center and Individual Assessment Ratings
Creator: Levin, Kelly
Date: 2023
Description: Organizations have become increasingly reliant on the use of assessment centers and individual assessments when making hiring decisions....
Show moreOrganizations have become increasingly reliant on the use of assessment centers and individual assessments when making hiring decisions. However, there has been relatively limited research on the prevalence of subgroup differences with the use of these tools. As such, this study investigated the individual characteristics of gender and age in both assessment centers and individual assessments. Specifically, the study examined how candidate gender and age, as well as the dyadic “match” between assessor-assessee gender and age impacted assessors’ hiring recommendations. Results from this research partially supported the hypothesized outcomes. Both female and younger candidates were rated more favorably on the overall assessment center recommendation. However, neither candidate gender or age had a significant impact on individual assessment recommendations. Further, demographic similarity had little effect on overall recommendations in both ACs and individual assessments. Implications and avenues for future research are discussed.
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Title: Linear Systems Analysis of Molecular Dynamics
Creator: Nicholson, Stanley Anselm
Date: 2023
Description: Most proteins reduce the complexity of atomic motion to stable and coherent structures. Molecular dynamics (MD) has provided swaths of...
Show moreMost proteins reduce the complexity of atomic motion to stable and coherent structures. Molecular dynamics (MD) has provided swaths of trajectory data of proteins. We analyze these trajectories using classical stochastic signal analysis, well established and utilized by engineers. Linear systems analysis operates to uncover linearities given an input and output signal. The coherence function says an input and output are linearly related if and only if the coherence equals one. Analyzing protein motion in the frequency domain allows us to extract a frequency function relating the modes of motion as determined by atomic power spectra. Motivated by biochemistry, we analyze classical interactions like hydrogen bonds and salt bridges and find they act like a linear system, or effective spring. We test our analysis on two protein systems: crambin and the Mu Opioid Receptor (MOR). We extend our results to all pairwise interaction and determine coherent communities of atoms within the MOR. We present various community detection algorithms and demonstrate their validity using common metrics in MD. Identifying rigid and tightly correlated regions of the protein offers great potential in coarse graining protein structure and understanding protein motion.
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Title: A Novel Explainability Approach For Spectrum Measurement Insight
Creator: Nagpure, Vaishali
Date: 2023
Description: Spectrum is an extremely valuable natural resource in high demand. Although the spectrum has been fully allocated, there is no comprehensive...
Show moreSpectrum is an extremely valuable natural resource in high demand. Although the spectrum has been fully allocated, there is no comprehensive method for understanding about how it’s being used. Spectrum measurements are highly complex spatiotemporal data sets that play a key role in understanding spectrum use and require very specialized domain information for understanding. To leverage existing and future spectrum measurements to the fullest extent, it is necessary to have a systematic way to connect them to the contextual information that helps provide meaning to the data. To analyze and interpret the measurements, a variety of contextual information is needed. This research develops a novel approach for spectrum measurement understanding that unifies five years of wideband spectrum measurement summary data together with relevant contextual information from a variety of sources in a spectrum knowledge graph. Both quantitative and qualitative information is modeled and implemented on a Neo4j graph database platform. This modeling formalizes the relationships that help spectrum stakeholders “connect the dots” and provide deeper understanding of RF spectrum utilization. The knowledge graph can be queried to extract a wide variety of insights thus making spectrum knowledge more widely accessible to a variety of stakeholders.
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Title: Pain and Life Satisfaction: The Role of Emotional Support and Social Participation in Adults with Pediatric-Onset Spinal Cord Injury
Creator: Oh, Alison J.
Date: 2023
Description: Individuals who acquire a spinal cord injury (SCI) during their pediatric years are unique from individuals who acquire an SCI during...
Show moreIndividuals who acquire a spinal cord injury (SCI) during their pediatric years are unique from individuals who acquire an SCI during adulthood due to the heightened risks for developing secondary health conditions and chronic pain. These concerns may significantly impede developmental milestones and experiences, such as social participation, that may decrease life satisfaction. Thus, research on pain characteristics (e.g., frequency of pain, duration of pain, level of current pain, and level of general pain) alongside the role of emotional support and social participation on life satisfaction in adults with pediatric-onset SCI is needed. This study sample consisted of 205 individuals with pediatric-onset SCI who were former patients at pediatric hospitals in three metropolitan cities. Participants were individuals who were 19 years of age or older at the time of the interview, with the age of injury at 18 years or prior. Associations between demographic, pain, and injury characteristics, emotional support, social participation, and life satisfaction were assessed via bivariate Pearson correlations and a 3-block hierarchical multiple linear regression with LS as the primary outcome. Although pain characteristics and emotional support were not associated with life satisfaction among adults who sustained a pediatric-onset SCI, social participation was positively associated with life satisfaction. These findings suggest that clinicians should evaluate various pain characteristics and highlight the role of social participation when treating individuals with SCI.
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Title: Examination of Power Ultrasound and Organic Acid-based Hurdle Technology in the Reduction of Salmonella Enterica on Peaches and Apples
Creator: Mathias, Hina Valida
Date: 2023
Description: Fresh produce includes fruit matrices like whole peaches and apples that are minimally processed and are a popular choice among different...
Show moreFresh produce includes fruit matrices like whole peaches and apples that are minimally processed and are a popular choice among different types of demographics because of their nutrition content and health benefits. However, there have been increasing pathogen outbreaks in these matrices over the past few decades, which are majorly rooted in cross contamination either due to poor handling pre and post processing or the insufficient reduction of the pathogen at processing by the applied hurdle technology. While chemical sanitizers are a popular option in the food industry, the awareness and demand for green consumerism and sustainability have created a need for research to determine the efficacies of organic acids and non-thermal technologies like power ultrasound in the reduction of different pathogens on different food matrices. This study focusses on the S. enterica reduction capabilities of three organic acids – citric, malic, and lactic alone and in combination with 40 kHz power ultrasound at 1, 2 and 5% for treatment times of 2, 5 and 10 min on whole yellow peaches and gala apples. Peaches and apples were spot inoculated with a four-strain cocktail of S. enterica, resulting in 9 log CFU/fruit. Post air drying for 1 h, the fruits were treated with water, 1, 2, or 5% citric, lactic, or malic acid for 2, 5 or 10 min with and without power ultrasound treatment at 40 kHz. The population of S. enterica on the fruits was enumerated before and after treatment. Three independent trials with triplicate samples were performed for each condition. Population differences were evaluated via Student's t-test and ANOVA; p<0.05 was considered significant. The initial level of inoculum ranged from 8.67 ± 0.41 to 8.20 ± 0.26 log CFU/peach and 7.28 ± 0.60 to 8.17 ± 0.37 log CFU/apple in peaches and apples, respectively. Water treatments showed pathogen reduction as high as 1.22 log CFU/peach and 1.02 log CFU/apple. Citric acid treatments on peaches showed significant pathogen reduction at higher time increments at 5% with a reduction of S. enterica as high as 2.24 log CFU/peach after 10 min. Malic acid showed the highest recorded log reduction in peaches at 5% and 10 min being 4.20 log CFU/peach (n=1/9, samples above the enumeration limit) and apples at 5% and 5 min being 3.71 log CFU/apple (n=4/9, samples above the enumeration limit) both in combination with an ultrasound. Lactic acid, unlike the other two organic acids, showed a pathogen reduction of over 3 log CFU/fruit at 2% after 10 min, with the highest pathogen reductions of 3.76 log CFU/peach and >3.62 log CFU/apple at 5% and10 min. There was no particular trend with significant enhancement of pathogen reduction either with time increment or the addition of ultrasound and varied with the varying acids, treatment conditions and fruit matrices.
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Title: Cities on the Move: Envisioning Cityscape in the Autonomous Vehicle Era
Creator: Lee, Hyo Eun
Date: 2023
Description: As cities have developed in response to a rapidly changing environment since the industrial revolution, a variety of circumstances and issues...
Show moreAs cities have developed in response to a rapidly changing environment since the industrial revolution, a variety of circumstances and issues have arisen. In cities, technological development and the rise of private automobiles have been related to urban sprawl, traffic congestion, environmental degradation, and mobility inequalities. Currently, autonomous vehicles are bringing a paradigm shift in mobility as technological innovations accompany the fourth industrial revolution. As cities undergo renewal through new mobility, recent developments in driverless technologies should be considered for their long-term impact on the built environment. The purpose of this thesis is to propose a design, along with a set of scenarios, for a transportation hub in the era of autonomous vehicles to address today's complex mobility requirements. Through bringing together existing case studies, research on autonomous vehicles, and drawing conclusions regarding the relationship between mobility and urban landscapes, this thesis explores how autonomous vehicles will affect the future built environment in a set of timelines. Considering the future transportation hub will require programs and infrastructure to support autonomous vehicles, this thesis also provides a vision of the future of transportation modes and how they interact. Finally, a transportation hub design model and an automation city model for the AV era will be proposed.
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Title: Analysis on the Persisting Effects of Redlining on Green Infrastructure in Chicago Neighborhoods - Neighborhood Map files
Creator: Meyer, Joel, Eshraghi, Amirreza
Date: 2023
Description: This compressed collection of map files constitutes data used in "Analysis on the Persisting Effects of Redlining on Green Infrastructure in...
Show moreThis compressed collection of map files constitutes data used in "Analysis on the Persisting Effects of Redlining on Green Infrastructure in Chicago Neighborhoods", published in SoReMo, March 2023 (DOI: https://doi.org/10.18409/soremojournal.v3i1.221). The report looks at “redlining” maps produced by the Home Owners’ Loan Corporation (HOLC) to analyze the current green infrastructure levels in various neighborhoods of Chicago.
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Title: Views on Ethical Issues in Research Labs: a University-Wide Survey
Creator: Laas, Kelly, Taylor, Stephanie, Miller, Christine Z, Brey, Eric M, Hildt, Elisabeth
Date: 2021
Description: Full survey used for "Views on Ethical Issues in Research Labs" published in the journal Accountability in Research: Policies and Quality...
Show moreFull survey used for "Views on Ethical Issues in Research Labs" published in the journal Accountability in Research: Policies and Quality Assurance in 2021. This survey was completed in 2017.
Sponsorship: National Science Foundation
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Collection: Accountability in Research: Policies and Quality Assurance

Title: Mentions of Ethics Codes in Social Media: A Twitter Analysis
Creator: Laas, Kelly, Hildt, Elisabeth, Wu, Ying
Date: 2022, 2022-01-03
Publisher: Springer, Cham
Collection: Codes of Ethics and Ethical Guidelines: Emerging Technologies and Changing Fields

Title: An Introduction to the Societal Roles of Ethics Codes
Creator: Laas, Kelly, Davis, Michael, Hildt, Elisabeth
Date: 2022, 2022-01-03
Publisher: Springer, Cham
Description: In this collected volume, we are interested in the roles of ethics codes and ethical guidelines in professions in which research and...
Show moreIn this collected volume, we are interested in the roles of ethics codes and ethical guidelines in professions in which research and innovation play an important role and where emerging technologies bring about considerable, sometimes fast-paced change.
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Collection: Codes of Ethics and Ethical Guidelines: Emerging Technologies and Changing Fields

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