Membrane distillation (MD) is a potentially economical water treatment process that can enhance water recovery and reduce the volume of concentrated brines produced by desalination processes. However, MD is yet to be implemented commercially because of several knowledge gaps that prevent scaling up MD. To use scaled up MD systems for the treatment of high salinity water, it is also vital to understand the effect of feed concentration and module size on water flux and energy consumption in MD compared to other water treatment technologies. Existing research and literature on improvements in MD have mainly been focused on analyzing MD in bench-scale systems. Very few studies have explored the effect of scale up on energy efficiency and water quality in MD. Furthermore, while several studies have explored new membrane materials in bench-scale systems, the applicability of new membranes in pilot-scale systems is often not investigated. The focus of this work is to highlight the key differences in operation between bench and pilot-scale that would enable efficient use of bench-scale data in predicting water flux and energy consumption in pilot-scale systems without the need for additional pilot-scale experiments. The activity of water is generally a weak function of salinity. However, because of the energy-efficient design of pilot-scale MD, the transmembrane temperature difference and hence vapor pressure difference in a pilot-scale system is small. Thus, increasing the feed salinity can lead to a negative or negligible vapor pressure difference in pilot-scale systems. The implications of reduced driving force when treating high feed salinity water on the minimum energy required in pilot-scale MD were explored in this work and compared with conventional processes such as reverse osmosis (RO) A model was developed to predict water flux and energy consumption of pilot-scale membrane distillation using a single parameter called membrane distillation coefficient determined using bench-scale experiments for two membranes. The pilot-scale model and membrane distillation coefficient method were validated using pilot-scale experimental data at a range of feed flow rates (2 to 4.5 L/min), temperatures (40 to 80 °C), and feed salinities (0 to 200 g/L NaCl). The validated model was then used to identify operating temperatures and flow rates that would maximize water flux and energy efficiency while treating high salinity water. For potable reuse of water treated using MD, the rejection of contaminants and pathogens in MD needs to be quantified. MD was shown to be a dual barrier treatment process for the reduction and rejection of pathogens because of the rejection of non-volatile contaminants and high operating temperatures (greater than 65°C). Two non-enveloped bacteriophages (MS2 and PhiX174) and an enveloped pathogenic virus (HCoV-229E) were used in this study. A bench-top temperature sensitivity study showed that at 65°C, the concentration of MS2 and PhiX174 reduced by more than 6-log10, and HCoV-229 reduced by 3-log10. Membrane rejection was measured to be greater than 6-log10 for MS2 and PhiX174, and greater than 2.5-log10 for HCoV-229E. The study also demonstrated limitations in using bench-scale systems for quantifying contaminant rejection. Membrane rejection of non-volatile components in a pilot-scale air-gap membrane distillation module was quantified using ultrafiltration permeate (or reverse osmosis feed) as feed. The rejection of pathogens in pilot-scale system was also studied using MS2 and PhiX174, which were used in the previous bench-scale experiments. The rejection of cations, anions, and organics was quantified at constant flowrate, inlet evaporator and condenser temperatures (70/30 °C), and different air-gap vacuum pressures. The rejection of viruses was quantified at lower temperatures (40/20 °C) and constant air-gap vacuum pressures. Rejection of organics, inorganics, and bacteriophages reduced with an increase in vacuum in the air gap, suggesting pore flow through pore defects. The rejection of both viruses was observed to be 0.5-log10 higher than the rejection of other analytes. The rejection of MS2 and PhiX174 at higher vacuum pressure and inactivation rate at high operating temperatures (70/30 °C) was investigated. Interestingly, exposure to high feed temperature (70 °C) did not lead to an instantaneous decrease in feed virus concentration, suggesting that longer residence times in the system are required to decrease virus concentration in the brine concentrate.

The FOXO family of transcription factors have several important roles in multicellular organisms: they are required for proper development of different tissues, maintain homeostasis in response to diverse cellular stresses, function as tumor suppressors, and have an evolutionarily conserved role in prolonging lifespan. Consistent with their role in diverse cellular processes, FOXO proteins are activated by several different stimuli, leading to the promotion of many different downstream programs often with opposing outcomes. How FOXO protein activation can lead to stimulus-dependent transcriptional outcomes is not known, though several mechanisms have been proposed. Possible mechanisms include differences in FOXO post-translational modifications, binding partners, and the dynamics of FOXO activation. Here, I will describe the current evidence in the literature supporting these mechanisms, and our investigation into the role of dynamic patterns of activation of the FOXO transcription factors. Specifically, I set out to determine whether FOXO responds to different stresses with different temporal patterns of activation. I have shown that FOXO responds to oxidative stress in a sustained, bimodal pattern, while it responds to serum starvation in a stochastic pulsatile pattern. I also found that in MCF7 breast cancer cells, both patterns are controlled by the activity of the primary negative regulator of FOXO, Akt.

An intraocular lens (IOL) is an artificial lens that is inserted into the eye as part of a treatment for cataract or myopia. Among the many types of IOLs, multifocal IOLs with diffractive optics design have been demonstrated to provide superior vision for both distance and near vision after surgery. In this dissertation, methods for estimating the diffractive surface profile of a multifocal diffractive IOL are investigated. Traditionally, several types of instruments have been proposed to determine the phase profile, such as conventional interferometers and Shack-Hartmann sensor. Holography-based setups have also been widely used for surface profile measurement. However, the proposed conventional methods require additional optical components or a reference beam, increasing system complexity and cost of the system. To avoid the limitations of the conventional system stated above, phase retrieval technique is implemented to estimate the diffractive surface profile of an IOL. The phase retrieval technique is the process of recovering the complex-valued function given the magnitude of its Fourier transform. It is natural to investigate the phase of an object, as optical imaging devices only measure the intensity of light and cannot measure the associated phase directly. This dissertation examines several phase retrieval algorithms. The multiplane phase retrieval algorithm described by Gerchberg is implemented for embodiment of the methods. To acquire multiple diffraction patterns, some techniques were used, such as displacing the imaging sensor to record intensities at different planes, modulating phase in Fourier domain using spatial light modulator (SLM) to record a sequence of intensities with different image planes, and modulating phase in pupil plane using SLM to record diffraction patterns at an image plane with phase diversity. Employing the considered phase retrieval schemes, the phase profile of diffractive multifocal intraocular lens was estimated. This dissertation begins with background section, which describes the human eye and basic principles of multifocal intraocular lenses. Chapter 1 covers basic optical structure of the human eye, age-related eye conditions, and fundamentals of intraocular lenses. Chapter 2 motives and describes several basic phase retrieval algorithms and limitations in finding the phase profile of the diffractive surface of an intraocular lens, followed by computational simulations. Chapter 3 deals with phase retrieval technique employed in combination with the multi-plane phase retrieval method, as well as the selected SLM-based phase retrieval technique suitable for finding phase profile of the diffractive intraocular lens. Chapter 4 describes the experimental setup employed for validating the phase retrieval technique to measure the wavefront profile of the intraocular lenses. The calibration procedure implemented in the experiment is discussed. Chapter 5 concludes the dissertation.

Scholars have recognized the importance of uncertainty as institutional context in collaborative water management, but the relationship between uncertainty and collaborative performance is mixed. On the one hand, increased uncertainty will positively enhance the performance of collaborative governance through new ties and innovations. On the other hand, certain types of uncertainties are negatively associated with collaborative performance. To understand the puzzle of uncertainty and collaborative performance, I take the problem of groundwater management for theory development and empirical testing. Aquifers are being depleted faster than they can recharge, leaving municipalities, irrigators, and ranchers vulnerable to ever-reducing water availability over time, but the management of groundwater problems is wicked because it involves complex social, ecological, scientific, administrative, and political issues. The effectiveness of collaborative governance depends in large part on the way in which stakeholders perceive, interpret, and use uncertain information.This dissertation fills the theoretical and empirical gap by using multi method research design. The first research question is: What are the nature and characteristics of uncertainty in collaborative governance? This question is addressed based on the in-depth case study of Upper San Pedro Watershed Partnership in Arizona, U.S. Based on the various sources of empirical data, including 22 in-depth interviews, policy reports, and local news articles, conceptual typology and theoretical propositions are proposed to develop theories of collaborative governance under uncertainties. Results suggest that scientific and managerial uncertainty are significant and tend to have negative effects on the performance of groundwater management, but the relationship between uncertainty and collaborative performance can be positively or negatively moderated by the quality of relation management including integrative leadership and cohesion building between participants. Results also suggest that levels and sources of uncertainty tend to change as collaboration evolves and thus the relationship between uncertainty and performance may shift over time. Having recognized that understanding scientific uncertainty is important in groundwater management based on the case study, this dissertation asks two questions: How and to what extent does scientific uncertainty affect collaborative performance? Do collaborative management tools have an impact on different types of collaborative outcomes, particularly under the condition of scientific uncertainty? This dissertation modified a groundwater game experiment where groups of 4-5 participants play a crop choice game for multiple rounds as resource users (Meinzen-Dick et al. 2016). The goal of this game for each participant is to grow as many profitable crops as possible under conditions where all users share groundwater resources with limited ability to recharge. But unlike the original game, where participants had full information about recharge rate, two treatments are introduced about scientific uncertainty in water recharge: uncertainty operationalized as a range of values (Treatment 1) and uncertainty operationalized as competing hydrological models (Treatment 2). Using quantitative and qualitative game experimental data from 30 groups, results suggest that more uncertain information tends to reduce individual earnings and thus increase shared resources. A range of uncertain information has a more significant impact on resource behavior than competing information. Finally, post-experimental analysis shows that diverse collaboration strategies tend to reduce distributional inequity among game participants. This dissertation contributes to the literature of collaborative governance and collective action by explicitly theorizing and modelling the relationship between uncertainty, collaboration process, and performance.

Why do some electoral districts experience more pre-election violence than the others in national elections? This dissertation examines how a particular type of local actor – African chiefs – affects pre-election violence locally. I make two arguments regarding the conditions under which chiefs are capable of deterring pre-election violence targeting their communities, and under which chiefs and their subjects are motivated to participate in pre-election violence. In my dissertation I first argue that centralization of kin groups in precolonial era enhances chiefs’ capability of voter coordination in contemporary time, and in turn reduces the risk of pre-election violence. Using survey- and event-based data from both existing and original datasets, I find a negative relationship between precolonial centralization of kin groups and pre-election violence. Further results of two-stage least squares regressions confirm the internal validity of this relationship. These findings apply to cases where indirect rule was adopted and customary land tenure preserved under colonial government, such as much of the Anglophone West Africa, because precolonial institutions have been better preserved in such cases. My second argument concerns how kin-group-based chieftaincy disputes drive royal families to fight one another during the election periods. Having local aspirants in the challenger families – who seek to change the status quo in chieftaincy disputes – increases the risk that chieftaincy disputes escalate into violent conflicts during the elections. Local aspirants, politicians who have dual identities as political party and royal family members, have particular interests in causing political parties to interfere with chieftaincy disputes. As the outcomes of chieftaincy disputes become associated with the outcomes of national elections, disputing royal families have strong motivation to fight each other during elections. I adopt a most-similar case design based on qualitative data collected through field research in Ghana, and inductively develop a theory of politicization of chieftaincy disputes. The findings of this dissertation demonstrate the complex functions of chiefs and their institutional foundations in African elections. In particular, the institutions of kin group structure local actors’ interests in such a way that they could be motivated to support and undermine democratic processes at the same time. These findings contribute new arguments and evidence to the debate about the relationships between traditional and democratic institutions. In addition, they also highlight the heterogenous colonial legacies between the Anglophone countries in Africa. Precolonial institutions are in general better preserved in Anglophone countries in West Africa then in other countries. Lastly, the findings also have policy implications. Chiefs can become valuable local non-state actors that join forces with international and national actors in pre-election violence prevention. It is also necessary to develop legal and policy instruments that separate politicians from traditional affairs.

Purpose: This project was designed to determine whether connecting immigrants with mental health practitioners and community resources could be beneficial for increasing awareness of such resources within the immigrant community. Background: Immigrants represent a large percentage of the population in the United States (US), accounting for over 44.8 million individuals. However, the immigrant population is disproportionally affected by mental health disorders, with 30.8% and 30.65% suffering from depression and post-traumatic disorder, respectively. Immigrants often lack access to mental healthcare due to frequent barriers, including undocumented status, lack of health insurance, low income, communication barriers, and coping behaviors, among other obstacles. Methods: The project adopted a quantitative, descriptive approach to determine the impact of connecting immigrants at a local clinic in Tucson, Arizona, with mental health providers and mental health community resources. Self-reported data were collected from 16 immigrants using a self-developed survey. The data was then analyzed using descriptive statistics. Results: The survey findings demonstrated an increased awareness of food banks providing groceries and an understanding of shelter organizations and self-help groups designed to address needs within the immigrant community. Linking immigrants to mental health providers increased their knowledge of available resources. The Wilcoxon Signed-Ranks Test yielded P<0.05, demonstrating a significant improvement in awareness of mental health resources. Conclusion: Connecting immigrants to mental health providers and community resources significantly improve immigrants’ awareness of mental health resources and strengthens a collaborative care approach compared to current practice

PurposeThe purpose of this quality improvement initiative was to provide hospice and palliative care education to healthcare providers to increase their willingness and intent to refer patients to them. Background Hospice and palliative care have been used for many years to improve patient quality of life by decreasing pain, anxiety, and overall symptom burden for patients suffering from terminal illnesses (American Cancer Society [ACS], 2019; Center to Advance Palliative Care, 2022). While these services have well established benefits for patients and their families, they are often not utilized until very late into a patient’s terminal diagnosis (Devi, 2011). There are many potential reasons for a lack of or late referrals to hospice and palliative care, but a healthcare provider knowledge deficit may play a major role. Methods An online educational PowerPoint session (OEPS) on hospice and palliative care was administered to the healthcare providers at the Bear River Valley Hospital, along with a follow up post-then-pretest survey. The healthcare providers were asked to answer questions about their knowledge of, attitude towards and perception of intent to refer to, and experiences with hospice and palliative care both before and after viewing the OEPS. The healthcare providers were asked to provide two Likert scale scores of 1 to 5 for each question, one being associated with before the education and one being associated with after. Results Eleven healthcare providers participated in the OEPS and follow up survey. There was an overall reported increase in Likert scale score for each question following administration of the OEPS. The greatest increase in score was noted for the questions regarding knowledge of and intent to refer patients to hospice and palliative care. Conclusions Hospice and palliative care education for healthcare providers has the potential to increase their intent to refer patients to these important services. This may translate to an increase in actual patient use of hospice and palliative care, potentially improving their outcomes.

The benefits of higher education for society are numerous: addressing many pressing social and environmental challenges requires a diverse workforce with advanced degrees; democracies thrive with knowledgeable and caring voters; and a productive economy needs a well-trained workforce. Consequently, increasing the number and diversity of students attending college is a forefront concern. However, gender and racial/ethnic differences in college enrollment and completion persist. Given the importance of higher education for individuals and society, understanding the factors underlying these differences is critical. Students’ expectations of success and how they perceive the value of education affect their post-secondary plans. Social identities and the sociocultural context influence the development of these expectations and values. In particular, an individual’s identified race/ethnicity, affiliated gender, motivational beliefs and perceived environmental support can enhance or detract from the formation of college aspirations. However, few studies have examined how these factors interact to affect intention to complete college. With a diverse sample of 4803 high school students from the 2018 Programme for International Student Assessment United States data, I used logistic regression and structural equation modeling to examine gender and racial/ethnic differences in the extent to which 1) motivational beliefs, including self-efficacy, perceived usefulness of education, mastery motivation, and sense of purpose predict college aspirations; 2) environmental support (i.e. parent support, sense of belonging, and bullying), mediated by motivational beliefs, predict college aspirations. Results show that each motivational belief significantly predicts college aspirations for students overall, but differences emerge when examining individual gender and racial/ethnic groups. High perceived utility of education and mastery motivation significantly increased college aspirations for Black and White students; self-efficacy and mastery motivation for Hispanic students; and high mastery motivation only for Asian American students. In addition, motivational beliefs fully mediate environmental support for Black, Hispanic, and White students, but not Asian American students. The structural equation model did not significantly explain the variance in college aspirations (R2 = 0.28, p = .31) for Asian American students, but it did significantly explain 36% (R2 = 0.36, p < .001) for Black students, 29% (R2 = 0.29, p < .001) for Hispanic students, and 34% (R2 = 0.34, p < .001) for White students. The study highlights the value of increasing high school students’ motivational beliefs, particularly mastery motivation, but it also indicates that harnessing existing motivation beliefs and environmental supports promises to foster college aspirations, ultimately benefitting both individuals and society.

The rapid growth of the Internet and Cloud Computing has started a data revolution, with data analytics playing a critical role in all areas, including transportation. Vehicles are increasingly relying on integration of data from an array of sensors (over different communication networks) to perform semi/fully autonomous operations. These vehicles use sensors like onboard cameras, LiDARs, Global Positioning System (GPS) deployed as Engine Control Units (ECUs) communicating over Controller Area Network (CAN) bus. As vehicles become increasingly autonomous, they will increasingly rely on V2X communications, including Vehicle to Vehicle communications, and Vehicle to Infrastructure communications, for information sharing and performing predictive analysis for a faster, safer, and more comfortable ride. However, this increased vehicle automation relying on sensors and communications has exposed them to cyberattacks. Researchers have successfully demonstrated attacks on the latest Jeep Cherokee, Tesla, and Lexus models, by disabling critical safety features in the vehicle, creating fatal accident-causing scenarios. Vehicle manufacturers rely (unsuccessfully) on encryption, obfuscation techniques, and individual intrusion detection systems to detect and prevent cyber-attacks. These approaches fail to secure the vehicles (as showcased in attacks on Tesla and Lexus), as these vehicular attacks are highly complex, where the attacker exploits multiple systems simultaneously. Such complex attacks are very hard to detect, and counter (once detected) as countermeasures taken to prevent the attack has the potential to disable a critical autonomous vehicle subsystem. Thus, there is a need for a security framework that takes into account a holistic perspective of vehicle operations before taking mitigating actions. This dissertation presents an Autonomous Vehicle Security Framework (AVSF) that uses a combination of highly granular anomaly behavior analysis based intrusion detection systems to detect cyberattacks on various vehicle subsystems. The proposed AVSF performs decision fusion of individual threats to obtain a global vehicle impact and risk analysis before taking mitigation actions to stop the cyberattack. To showcase the efficacy of the proposed AVSF, I present a threat model analyzing the potential threats (and attack vectors) targeting autonomous vehicles and present three anomaly-based intrusion detection systems to detect attacks on different components of autonomous vehicles. For the threat modeling process, I present an autonomous vehicle architecture that splits the complex vehicle into four layers: End Devices layers, Intra Vehicle Communications layer, Inter-Vehicle Communications layer, and applications layer. The presented threat model analyzes and rates each threat on every layer in the autonomous vehicle architecture using the DREAD framework. This dissertation presents an Intrusion Detection System to detect attacks on vehicle sensors and actuators (End Devices). This sensor intrusion detection system (IDS) models the sensor's normal behavior using Discrete Wavelet Transform (DWT). The DWT uses Biorthogonal, Daubechie, Coiflets, Discrete Meyer, Reverse Biorthogonal, and Symlets wavelets to model the spatial and temporal features of the sensor. Experimental analysis shows the IDS can detect attacks like Denial-of-Service attacks, Impersonation Attacks, Random signal attacks, and Replay attacks with One-Class SVM, Local Outlier Factor, and Elliptical Envelope. The One-Class SVM performed the best compared to the results of other machine learning techniques. This dissertation presents a second IDS to detect attacks on the Bluetooth Protocol (Inter-Vehicle Networks). This Bluetooth IDS uses an n-gram based approach to create a behavior model for characterizing the normal behavior of the AV using the Bluetooth protocol by monitoring the protocol's state transitions. Attacks on the Bluetooth network are detected using machine learning algorithms like Decision Trees, AdaBoostM1, SVM, Naïve Bayes, Ripper, and Bagging algorithm, with precision up to 99.6% and recall up to 99.6%. Lastly, I present an IDS to detect attacks on CAN-BUS (Intra Vehicle networks). Attacks on the CAN bus are detected using novelty detection algorithms like SVM, Isolation Forest, Local Outlier Factor, and Elliptic Envelope. I also use two class classifiers like SVC, Multilayer perceptron, Decision tree, Random Forest and AdaBoost to classify the data with precision up to 100% and recall up to 100%.

Purpose. This quality improvement project aimed to promote structured diabetic foot examination, promote compliance, and perform appropriate documentation at the American Family Medicine (AFM), Tolleson location. Background. Type 2 diabetes mellitus is a significant public health issue that affects over 37.3 million people nationally. The number is expected to grow exponentially as many are undiagnosed. In Arizona, there has been an increase of 10% from 2011 to 2016 with many more categorized as prediabetic. A common complication of diabetes is diabetic neuropathy which can contribute to life-threatening diabetic foot ulcers. Screening for loss of protective sensation with a 10-g monofilament can decrease mortality and morbidity associated with sequela of infections or amputations. Methods. Participants were clinicians and support staff at American Family Medicine, Tolleson. The Plan-Do-Study-Act (PDSA) cycle guided implementation. An in-service was provided, including evidence-based information regarding the purpose of a comprehensive diabetic foot exam, the use of monofilament, and participant roles/expectations for implementation. Data was collected via surveys post-in-service and after two weeks of implementation to evaluate change. Data analysis was analyzed using Microsoft Excel and reported with descriptive statistics. Results. The project implementation period was two weeks at AFM. The resulting evaluation responses indicate that the in-service improved knowledge and attitudes regarding utilization. Before implementation, there was no structured foot exam documentation or monofilament use. Post-implementation, all staff members providing direct care increased the use of structured foot exams with a monofilament tool. Conclusions. An annual foot assessment with a monofilament tool is an evidence-based screening tool that reduces complications such as ulcers and amputations among diabetic populations. It is also a cost-effective method for diagnosing loss of sensation and peripheral neuropathy and initiating conversation regarding the importance of foot care. An interdisciplinary approach is also beneficial when implementing this change.