Electronic Thesis or Dissertation

Urban freight system constitutes an essential component for both economic and social aspects of the urban areas. However, the driving forces of globalization and ecommerce have adversely affected the volume of freight vehicles in urban roads over the past few decades impacting the sustainability and efficiency of last-mile deliveries. At the same time, the last-mile problem of goods distribution from companies to customers comprises one of the most costly and highest polluting components of the supply chain. Over the past few years, different innovative concepts of autonomous vehicles were introduced to improve last-mile logistic inefficiencies such as traffic congestion and pollution externalities. The objective of this study is to optimize a distribution network consisting of a set of depots and customers by utilizing Autonomous Delivery Robots (ADRs). For that reason, a Mixed Integer Linear Programming model was developed in GAMS for solving the vehicle routing problem while minimizing the total delivery and delay costs of ADRs. This optimization model is based on the route assignment and the required number of ADRs within the network. A heuristic solution algorithm based on the cluster-first, route-second technique was developed in MATLAB for solving the NP-hard problem efficiently. First the customers were clustered to depots based on their maximum distance from them and the maximum allowed number of customers per cluster. After the clustering, the mathematical model was implemented in each cluster providing an exact solution. Three different medium-sized scenarios of 200, 300 and 400 customers were tested under three different clustering instances of a maximum of 20, 30 and 40 customers per cluster and their results were presented and discussed in detail.

The ultimate challenge for assisted reproductive technologies (ARTs) is to select the most competent sperm population from a semen sample in an efficient way. In this thesis, we report on an effective sperm sorting microfluidic device that exploits the rheotaxis of sperm and investigates the sperm quality sorted under various flow conditions. Rheotaxis is the ability of a sperm cell to orient itself in the direction of the flow and swim against it. We developed a novel passively driven pumping system that provides a steady flow rate while it requires no external power source. We have also developed another rheotaxis-based microfluidic device that washes out the raw semen sample from any dead or less motile sperm. The device consists of a collection and waste chamber. To evaluate the effect of the shape and height of the collection chamber, we measured the sperm motility and velocity parameters after sorting using varying the shape and height of the collection chamber. We demonstrated that sperm selected with all devices have higher motility, normal morphology, and a fewer degree of DNA fragmentation compared to a control group.

The purpose of the current study was to examine differences in multidimensional perfectionism, help-seeking, negative affectivity, and social-emotional well-being between grade 9 to 12 early college high school students who received the modified version of Cognitive Behavioral Therapy for Perfectionism (CBT-P) small group counseling intervention (Egan et al., 2014a) and grade 9 to 12 early college high school students in the comparison group who did not receive the intervention. Masters-level counselors in training (CIT) implemented CBT-P with early college high school students after being trained in the use of the program and other study-related procedures. The study followed a quasi-experimental, non-equivalent pre-post design and employed various self-report measures (DASS-21, SEHS-S, CAPS, and GHSQ). A series of ANCOVA analyses were conducted to determine statistically significant differences between the treatment and comparison groups. The researcher reported partial eta squared ŋp2 effect size for each independent variable. Results of the study revealed a statistically significant difference in negative affectivity and self-oriented perfectionism between the treatment and the comparison group. However, no statistically significant difference, by treatment condition, was found regarding participants’ socially prescribed perfectionism, help-seeking intentions, or social-emotional well-being. The modified CBT-P treatment has found large effects (ŋp2 = .219) in reducing negative affectivity as measured by the DASS-21 and medium to large effects (ŋp2 = .115) in reducing self-oriented perfectionism. This study provided clinical support for using the modified CBT-P small group intervention (Shafran et al., 2002) in early college high schools to decrease negative affectivity and perfectionism in students. Furthermore, the study further supports the importance of building social-emotional wellness to improve students’ mental health. Finally, it highlights the need for future research to determine the impact of perfectionism and small group interventions on early college high school students’ mental health, wellbeing, and help-seeking behaviors.

Since the population growth systems may suffer impulsive environmental disturbances such as earthquakes, epidemics, tsunamis, hurricanes, and so on, stochastic differential equations(SDEs) that are driven not only by Brownian motion but also by α-stable Lévy noises are more appropriate to model such statistical behavior of non-Gaussian processes with heavy-tailed distribution, having infinite variance and in some cases infinite first moment. In this dissertation, we study stochastic processes defined as solutions to stochastic logistic differential equations driven by multiplicative α-stable Lévy noise. We mainly focus on one-dimensional stochastic logistic jump-diffusion processes driven by Brownian motion and α-stable Lévy motion. First, we present the stability analysis of the solution of a stochastic logistic growth model with multiplicative α-stable Lévy. We establish the existence of a unique global positive solution of this model under certain conditions. Then, we find the sufficient conditions for the almost sure exponential stability of the trivial solution of the model. Next, we provide parameter estimation for the proposed model. In parameter estimation, we use statistical methods to get an optimal and applicable estimator. We also investigate the consistency and asymptotics of the proposed estimator. We assess the validity of the estimators with a simulation study.

To address the increased interest in crypto hardware accelerators due to performance and efficiency concerns, implementing hardware architectures of different public-key cryptosystems has drawn growing attention. Pure hardware methodology enhances architecture’s performance over a hardware/software co-design scheme at the cost of a more extended design cycle, reducing the flexibility, and demands customized data paths for different protocol-level operations. However, using pure hardware architecture makes the design smaller, faster, and more efficient. This dissertation mainly focuses on designing crypto accelerators that can be used in embedded systems and Internet-of-Things (IoT) devices where performance and efficiency are critical as a hardware accelerator to offload computations from the microcontroller units (MCU). In particular, our objective is to create a system-on-chip (SoC) crypto-accelerator with an MCU that achieves high area-time efficiency. Our implementation can also be integrated as an off-chip solution; however, other criteria, such as performance, are often as important or more important than efficiency in the external crypto-chip design, which is beyond of this work. Not only does our architecture inherently provide protection against timing and simple power analysis (SPA) attacks, but also some advanced security mechanisms to avoid differential power analysis (DPA) attacks are included, which is missing in the literature. In a nutshell, the contributions are summarized as follows:

The detection of rebar corrosion in reinforced concrete is important due to the high costs of corrosion related damages to infrastructure. One such method of rebar corrosion lies in the use of non-destructive ultrasonic testing. To date, acoustic methods require either the training of an artificial neural network or a theory of acoustic wave propagation. Using a more complete acoustic model such as the Biot-Stoll model avoids algorithm training requirements by directly modeling the acoustic environment. A problem with this method lies in the complexity of the model and the selection of free parameters. The problem of parameter selection is addressed by a series of targeted measurements using ultrasonic transducers on a set of existing reinforced concrete samples placed in a saltwater solution. This data can then be analyzed by a non-linear least squares solver to produce a better fit for the acoustic signal.

The coastal system provides habitat, storm protection, and economic value. In particular, Florida’s beaches are subject to chronic coastal erosion resulting from natural and anthropogenic influences. The most common mitigation response is the nature-based solution of beach nourishment. While this method is widely considered effective, quantifying changes from the dredge and placement on the physical environment is critical to ensure best management practices. The first step in addressing the need to identify gaps in knowledge relating to natural and human-induced changes to the continental shelf, a comprehensive literature review of the US East and Gulf coast continental shelves was conducted identifying needs for more expansive sand searches, a greater understanding of storm impacts on shelf morphodynamics, planning for long-term use of offshore sediment sources, and the impact of dredging on habitats. This study then evaluated the northern Palm Beach County beaches adjacent to the Jupiter Inlet over multiple years to understand the effects of natural and human influence on the morphology and sedimentology of the beach-nearshore environment. Beach sediment was coarser near the Inlet and finer downdrift (south). Seasonal changes in the nearshore from storms decreased the grain size and eroded beaches, whereas nourishment increased grain size and expanded beach width. Influences of physical characteristics of the beach-nearshore environment on the ecosystem were examined based on two important marine species: loggerhead sea turtles and blacktip sharks. No adverse impacts from restoration activities were found on loggerhead reproductive success. However, the active 2020 hurricane season resulted in lower reproductive success metrics. The blacktip shark migration coincides with the typical nourishment construction window. High turbidity in the nearshore was documented in association with multiple nourishment events during the two-year study. The blacktip sharks were quantified in the nearshore south of the nourishment; however, whether the turbidity was influencing the shark aggregates or habitat preference remains unknown. These results support numerous benefits of beach nourishment but suggest further research is needed to evaluate how project construction may impact nearshore fauna. The findings of this study are important for coastal managers who may consider reviewing best management practices of the beach-nearshore system.

In the U.S., an estimated 16 million persons provide unpaid care for family and friends with Alzheimer’s disease and related dementias (ADRD). These caregivers are experiencing challenges, such as lack social interaction, which further impacts their own health. Social isolation for caregivers is now considered to be another challenge due to living in a global pandemic.
The purpose of this study was to address the gap in understanding rural informal caregiver by examining social connectedness through the use of story-guided dialogues among rural caregivers of PWD during a global pandemic. Story Theory guides intentional dialogue, to bring forward connecting with self-in-relation through use of story path, noting low, high, and turning points.

Alzheimer’s disease is a neurodegenerative disease that causes cognitive dysfunction and leads to progressive memory loss and behavioral impairment. About 60% to 80% of dementia cases are attributed to Alzheimer’s disease and currently afflict about 50 million people worldwide. Although it primarily affects people over the age of 65, a person’s risk for developing Alzheimer’s disease earlier can depend on factors such as a family history (genetic inheritance) or experiencing an ischemic stroke event. Current treatments for Alzheimer’s disease include behavioral therapy and drug treatment that can lessen the severity of symptoms but cannot stop progression indefinitely. Sulindac is a non-steroidal anti-inflammatory drug that, by a mechanism independent of its anti-inflammatory properties, has shown to express a preconditioning response to protect from oxidative damage. Granulocyte colony stimulating factor is a hematopoietic glycoprotein that can stimulate the production of granulocytes and stem cells that has proven to provide neuroprotection in models of ischemic stroke via mechanisms including anti-apoptosis and anti-inflammation. In this in vitro study, the potential neuroprotective effects of Sulindac is measured against the effects of oxidative stress when subjected to hypoxia and reperfusion. Regarding un-transfected SHSY-5Y cells, hypoxia was demonstrated to lower cell viability starting at a period of 12 hours. It was found that a low concentration of Sulindac (200 uM) was effective in protecting SHSY-5Y cells against oxidative stress and overall lowering the rate of cell death in the event of hypoxic and reperfusion injury. When SHSY-5Y cells were transfected with Swedish APP mutation, cell viability was also markedly decreased in hypoxic conditions. However when treated with a concentration of 600 uM of Sulindac, cell viability levels were near matched with its normoxic counterparts

Human knowledge is acknowledged as critically important to economic growth and prosperity. Economists focus on the past few decades’ emergence of a knowledge-based economy greatly dependent on individual-level knowledge. Knowledge is a key resource of many organizations, and the need for an educated workforce is believed to facilitate the creation, share, and use of firm-level knowledge going forward.
An economy where knowledge is the main asset is very different from traditional production systems that depend on tangible assets. These tangible assets often rely upon scarce resources such as minerals, thereby forcing price fluctuations and potential disruptions in inventory and sales. Logistics and supply chain issues can dwindle as we have experienced during the recent pandemic. However, when knowledge is the firm’s main asset, the firm’s intangible asset will not decrease as sales increase. Knowledge also does not spoil or dwindle over time. Instead, knowledge will grow and evolve, and as the philosopher Aristotle once stated, “the whole is greater than the sum of its parts”. The fact that knowledge as the main asset does not decrease as a result of production makes the knowledge economy an interesting phenomenon to study and to understand.