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Social media such as Twitter enable people to interact with each other and share health-related concerns in a new and novel way, as evidenced during the COVID-19 pandemic when in-person communication became inconvenient under social-distancing policies. Little attention has been paid to the impacts of health policy and local political ideology on the trends of spatiotemporal emotions related to COVID-19. This study examines 1) the spatial-temporal clustering trends of negative emotions (or spillover effects); 2) whether health policies such as social distancing policy are associated with spatiotemporal emotion patterns towards COVID-19. This article finds that: 1) COVID-19 related negative emotions detected by social media have spillover effects and that 2) counties with staying at home policy or counties which are predominantly democratic exhibit a higher rate of negative emotional tweets toward COVID-19. These results suggest that scholars and policymakers may want to consider the impacts of interventions caused by public policy and political polarization on spatial-temporal patterns of public health concerns detected by social media.
This quantitative study sought to operationalize academic resilience through social engagement, family support, capacity for tolerance, and commitment to college among African American first year males at a private, urban HBCU in the United States. This study utilized secondary data from Ruffalo Noel Levitz's College Student Inventory Survey (CSI) Form B administered to incoming first year students over a five-year period from 2011 to 2015. The sample included 223 African American first year male participants. Utilizing a binary logistic regression analysis, the researcher examined the relationship between participants' outcomes and various factors, including social engagement, family support, capacity for tolerance, and commitment to college as a manifestation of academic resilience. The key outcome variables of this study were graduation and retention. Binary logistic regression analyses were conducted in SPSS version 27. Recommendations based on findings are provided for HBCU senior administrators, HBCU faculty, families of African American males, and future research.
This quantitative study examined the awarding of the Seal of Biliteracy (SoBL) in North Carolina public schools. Specifically, the study explored through a multiple logistic regression if the intersectionality of race, language, and class was related to whether a district did or did not award students the SoBL. The dependent variable of total student enrollment was also included. Additionally, within districts found to award the SoBL, this study examined through a multiple linear regression if the variables of race, language, and class related to the rate of graduating seniors who received the SoBL recognition. Total student enrollment was also included as a dependent variable. Results from the multiple logistic regression revealed that total student enrollment, while controlling for language, race, and class was related to whether a district did or did not award the SoBL. Within districts that awarded the SoBL, results from the multiple linear regression revealed that while controlling for race, class, language, and total student enrollment, class was negatively related to the rate of seniors who received the SoBL while language was positively related. Results are discussed through the theoretical framework of critical race theory, and salient recommendations are provided for the future.
Diet diversification has been shown both to improve nutritional health outcomes and to promote greater enjoyment in food consumption. Conversational Recommender Systems (CRS) has a rich history in direct recommendation of recipes and meal planning, as well as conversational exploration of the possibilities for new food items. But more limited attention has been given to incorporating diversity outcomes as a primary factor in conversational critique for exploration. Critiquing as a method of feedback has proven effective for conversational interactions, and diversifying recommended items during the exploration can help users broaden their food options, which critiquing alone may not achieve. All of these aspects together are important elements for recommender applications in the food domain.
\par This dissertation explores incorporating diversity in a critique based conversational recommender system to support diet diversification. Recommender systems are known to support the task of exploitation while diversity supports the task of exploration. Using a conversational recommender, this dissertation maintains this balance by enabling the exploration through critiquing, and maintains the exploitation by selecting the closest recommendation to the user profile. To enable this balance this dissertation introduces an interactive critique based conversational recipe recommender system called \textit{DiversityBite}, a novel way of dynamically generating critique during recipe recommendation.
\par The contributions of this dissertation are: (i) Development of a novel approach of dynamic diversity-focused critique for conversational recommender system, (ii) Applying dynamic diversity-focused critique in recipes domain to support diet diversification while exploring, and (iii) Identification of recipe features that are helpful in finding diverse recipes using dynamic critique. This study reports on three studies to show the potential of using dynamic critique in increasing diversity. The user studies considered for this dissertation are simulation study, and two user studies. These studies investigate if \textit{DiversityBite} can improve diversity in recipe recommendation.
Co-creation is a form of collaboration in which partners share, improve and blend ideas together to develop a creative product. It helps to share ideas and solve problems in a creative manner. Several co-creativity research works have focused on generating creative artifacts, but there is a limited amount of research in analyzing creative collaborations. Creative collaboration can be evaluated through examining interaction dynamics such as cognitive states, behavior, and the number of ideas generated. This dissertation conducted collaborative experiments to add a new contribution to human-human co-creation by quantifying and evaluating co-creativity using divergent and convergent thinking modes. We conducted 21 dyadic user studies of a turn-based collaborative drawing task to quantify and extract several co-creation patterns and compare co-creativity of users. The results of both studies showed significant differences of creative thinking between high and low creative performance. High co-creativity groups show balanced divergent and convergent thinking compared to other works. The interaction dynamics of different creativity levels were also different in terms of the number of ideas and objects created and modified. The work can be applied to different co-creation applications, and can be the starting point toward designing a computational creative thinking model in the future.
Realizing the next-generation electronic devices with added features, i.e., flexibility, smaller dimension,
higher density (transistors per unit area), lightweight, and low-power consumption would require extensive
work to optimize the processing conditions that would yield high-quality Si wires (microwire/nanowire)
with optimum device performance at an affordable price. To this end, we employed a cost-effective
lithography-free de-wetting technique to fabricate the seed layer for the growth of highly ordered Si
microwires (Si MWs). A quantitative analysis of the impact of various growth parameters on Si MW size
has been reported. This has important implications since the optoelectronic properties of a wire
configuration are strongly dependent on its size and the quality of the as-grown wires, thereby affecting the
device performance. An exponential dependence of MW growth rate has been reported, and the rate-
limiting step has been determined. The electrical transport properties of as-grown Si MWs have been
extracted via two-probe and three-probe measurements. Temperature-dependent IV measurements have
been done to determine the trap state density and trap energy level in the as-grown and passivated Si MWs.
Lastly, we demonstrate an easily constructed, single wire near-infrared (NIR) photodetector device with an
enhancement observed in responsivity, detectivity, and % EQE of low-powered Si MW by a factor of 44.8,
6.8, and 46.7 at the lowest applied voltage.
The Solid-State Circuit Breaker (SSCB), as an emerging semiconductor-based circuit protection technology, is featured with its extremely fast fault interruption/isolation speed and regarded as a promising alternative to the electromechanical circuit breakers in the DC systems. However, in the conventional SSCBs, large surge voltages are clamped across their semiconductor switches when the breakers open and the dynamic voltage unbalance is incurred when the series-connected switches are used. With these technical defects, the efficiencies and reliabilities of the SSCBs are impaired and their wide adoption to the DC distribution systems is set back.
To overcome these technical limits of conventional SSCBs, four types of Multilevel and Surgeless Solid-State Circuit Breakers have been proposed in this dissertation. By utilizing the fast switching speeds of the semiconductor switches, the proposed SSCBs can commutate the fault current to the different conduction paths of the circuit breakers and attain significant benefits on efficiency and fault isolation speeds in comparison with the conventional SSCBs. Particularly, for the proposed Multilevel Solid-State Circuit Breaker (MLSSCB), the series-connected switches are clamped to their voltage dividing capacitors during their switching transience and then the dynamic voltage unbalancing issues among the switches can be averted. For the proposed surgeless SSCBs, with surge voltage suppressed, the semiconductor switches do not need to be overdesigned for the voltage ratings and the conduction efficiencies of the SSCBs can be improved on the ground that the semiconductor device with higher voltage block capability has thicker drift regions and larger on-state resistance. Derived from the integration of the Ground-Clamped Surgeless SSCB and the Multilevel SSCB, the proposed Surgeless Multilevel SSCB (SMLSSCB) can solve both the surge voltage and dynamic voltage unbalancing issues in the medium voltage DC SSCBs and attain higher efficiency and an ultra-fast isolation speed prior to the other SSCBs. A fault-tolerant configuration of the SMLSSCB has also been proposed to improve the reliability of SMLSSCB and make it prior to that of the conventional SSCBs.
In this dissertation, the operating principles of the proposed SSCBs have been presented. Besides, to demonstrate the proposed SSCBs’ advantages over the conventional SSCBs on fault isolation speeds, power efficiencies and reliability, the comparisons between the proposed SSCBs and their counterparts of the conventional SSCBs have been made in terms of several key parameters of the circuit breakers. Additionally, the simulation/experiment results and design considerations of the proposed circuit breakers have been introduced to validate their technical feasibilities and practical uses.
The conventional method of suture ligation of vascular tissues during surgery is time consuming, skill intensive, and leaves foreign objects in the body. Energy-based radiofrequency (RF) and ultrasonic (US) devices have recently replaced the use of sutures and mechanical clips, providing rapid hemostasis during surgery. These devices expedite numerous labor-intensive surgical procedures, including lobectomy, nephrectomy, gastric bypass, splenectomy, thyroidectomy, hysterectomy, cystectomy, and colectomy. Though these newer methods provide rapid and efficient blood vessel ligation, both US and RF devices have limitations including the potential for unacceptably large collateral thermal damage zones, with thermal spread averaging greater than 1 mm. This lack of specificity prevents the use of these devices for delicate surgical procedures performed in confined spaces (such as prostatectomy). These devices may also cause thermal damage to healthy tissue through unintended heat conduction in contact with the device jaws. The active jaw of US devices can reach temperatures in excess of 200 oC during a single application and can take greater than 20 s to cool to usable temperatures before proceeding with further applications. The maximum temperatures on the active jaw of RF devices are lower (< 100 C), however, larger thermal spread is observed. This study explores the development of a novel alternative method using near-infrared (IR) lasers for vessel ligation, bisection, and real-time feedback during procedures. This dissertation focuses on the sealing (the act of permanently fusing the lumen of the vessel) and cutting (the act of bisecting a vessel) of the arteries (1-6 mm in diameter), which are the most common vessels sealed with an energy-based device during laparoscopic surgery. There are several potential advantages of laser-based sealing and cutting of vascular tissues compared to conventional US and RF energy-based devices. These include: (1) More rapid sealing and cutting of vascular tissues with seal and cut times as short as 1 s each; (2) More directed deposition of energy into tissue with collateral thermal spread of less than 1 mm; (3) Stronger vessel seals with higher burst pressures (up to 1500 mmHg); (4) An integrated device capable of both optical sealing and cutting of vascular tissues without the need for a separate deployable mechanical blade to bisect tissue seals; (5) Safer thermal profile with lower jaw peak temperatures (< 60 C) compared to ultrasound (~ 200 C) and radiofrequency (~ 100 C) devices; (6) Sealing of large blood vessels greater than 5 mm; and (7) An entirely optical based system with real-time quantitative feedback indicating the success of the thermal seal and/or bisection of the blood vessel. This dissertation will explore these advantages for laser-based technology along with an optical method for real – time optical feedback all with the capability of laparoscopic probe integration.
This qualitative study investigates the perceptions of high school-level alternative school administrators in North Carolina about the impact of performance standards (ESSA) on them and their campuses. The study interviewed four alternative high school administrators who are currently serving in alternative schools in North Carolina that were labeled Comprehensive Support and Improvement during the 2018-2019 school year due to low graduation rate and/or low performance. Analysis of the data resulted in the following findings: Alternative high school administrators perceive the ESSA guidelines for school performance as unfair and inequitable to alternative schools, and the guidelines demonstrate a lack of understanding of alternative schools and alternative education on the part of those who develop accountability guidelines.
The impact of these guidelines has resulted in changes to some practices on their campuses and receiving additional funding. Administrators perceive these accountability standards have no impact on their professional career but have increased their stress. Findings indicate that alternative school administrators perceive that the people who assist and are involved in the lives of students, their academic and social-emotional interventions, appear to be the most important strategies that can lead to successful outcomes for their students. Conclusions include a need for more awareness of the differences between traditional and alternative schools, the students served on these campuses, and more awareness of equity in education, specifically accountability and school performance.
Deep Learning (DL) has been widely applied in both academia and industry. System innovations can continue to squeeze more efficiency out of modern hardware. Existing systems such as TensorFlow, MXNet, and PyTorch have emerged to assist researchers to train their models on a large scale. However, obtaining performant execution for different DL jobs on heterogeneous hardware platforms is notoriously difficult. We found that current solutions show relatively low scalability and inefficiencies when training neural networks on heterogeneous clusters due to stragglers and low resource utilization. Furthermore, existing strategies either require significant engineering efforts in developing hardware-specific optimization methods or result in suboptimal parallelization performance. This thesis discusses our efforts to build an efficient and scalable deep learning system when training DL jobs in heterogeneous environments. The goal of a scalable learning system is to pursue a parallel computing framework with (1) efficient parameter synchronization approaches; (2) efficient resource management techniques; (3) scalable data and model parallelism in heterogeneous environments;
In this thesis, we implement robust synchronization, efficient resource provisioning approaches, asynchronous collective communication operators, which optimize the popular learning frameworks to achieve efficient and scalable DL training. First, to avoid the "long-tail effects" for parallel tasks, we design a decentralized, relaxed, and randomized sampling approach to implement partial AllReduce operation to synchronize DL models. Second, to improve GPU memory utilization, we implement an efficient GPU memory management scheme for training nonlinear DNNs by adopting graph analysis and exploiting the layered dependency structures. Third, to train wider and deeper Deep Learning Recommendation Models (DLRMs) in heterogeneous environments, we propose an efficient collective communication operator to support hybrid embedding table placements on heterogeneous resources and a more fine-grained pipeline execution scheme to improve parallel training throughput by overlapping the communication with computation. We implement the proposed methods in several open-source learning frameworks and evaluate their performance in physical clusters with various practical DL benchmarks.
