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Behavioral and Social Science (BSS) classes in higher education provide students with understandings of human behaviors, motivations, and actions that are crucial to confronting both social and personal problems. Moreover, most community college degrees require that students take at least one BSS class—anthropology, economics, political science, psychology, and/or sociology. While BSS classes are important—both from a philosophical as well as a degree-requirement standpoint—without effective student engagement, that importance may be lost. Oftentimes, BSS classes are still taught largely through didactic instruction. Yet, active and collaborative learning has proven to be a more effective instructional approach. Moreover, the need for active and collaborative learning may be even more crucial in community college BSS classes due to the unique demographics of these institutions. Building on findings that active and collaborative learning in BSS classes is more effective than didactic instruction, the purpose of this study is to better understand BSS instructors’ knowledge of active and collaborative learning and to identify the factors that foster this instructional approach and those that present hurdles. By determining these factors, recommendations can be made for replicating effective active and collaborative learning in BSS classrooms and/or working to minimize the roadblocks to this instructional approach.
Background
Sugammadex is one of the most used agents to reverse surgery induced neuromuscular blockade. It also reduces hormonal contraceptive effectiveness. There is limited evidence about developing effective education of the interaction between Sugammadex and birth control pills in post anesthesia care unit (PACU) nurses who are responsible to provide discharge education to patients taking hormonal contraceptives.
Purpose
The purpose of the project is to examine the effect of a web-based education on PACU nurses’ awareness and knowledge about the interaction between Sugammadex and hormonal contraceptives.
Methodology
A quasi-experimental study with pretest posttest design was conducted in a southeast healthcare facility. An animated educational video was designed to enhance the knowledge about the interaction between Sugammadex and hormonal contraceptives. An online survey was performed to assess the change of pre and post education knowledge score.
Results
The study included 27 PACU nurses. Age was associated with pretest score (b = 0.04, p = .010). Older participants were likely to have higher pre-intervention score. There was a significant pretest -posttest difference on Question 1 (the mechanism of action of Sugammadex) (χ2 = 7.72, p = .005) and total score (3.93± 1.00 vs. 4.55 ± 0.67, t = 2.81, p = .011).
Conclusion
The web-based education is effective to improve PACU nurses’ knowledge of the drug interaction between Sugammadex and hormonal contraceptives. The finding may help the development of discharge teaching in surgical patients taking hormonal contraceptives.
Background
Sugammadex is an effective and safe drug to reverse surgery induced neuromuscular blockade. However, the interaction of Sugammadex and hormonal contraceptives may lead to unintended effects in surgical patients who are using hormonal birth control. There is insufficient evidence regarding the effectiveness of online education among post-anesthesia care unit (PACU) nurses, who are responsible for delivering discharge education.
Purpose
The purpose of the project is to examine the effect of a web-based education on PACU nurses’ awareness and knowledge about the interaction between Sugammadex and hormonal contraceptives.
Methods
We conducted a quasi-experimental study with pre-/post-test design. The study was conducted in a surgical center located in the southeastern region of the United States. A digital survey was given both prior to and following the online educational session. The effects of web-based education was examined by comparing the differences of the pre and post-educational survey knowledge scores.
Results
Among 19 PACU nurses, their years of experience ranged from 0 to 33 years (M = 5.67, SD = 8.81, Mdn = 3). There was a significantly increased number of corrections between pre- and post-test from 4.37 ± 0.90 to 4.81 ± 0.40 (t = 2.52, p = .023).
Conclusion
The online education proves to be both feasible and effective in enhancing the knowledge of drug interactions among PACU nurses. This finding could contribute to the development of a standardized educational framework for ongoing drug education in nurses.
Background
Sugammadex is a useful reversal agent of neuromuscular blockade during surgery. However, its interaction with hormonal contraceptives could lead to undesirable outcomes in surgical patients using birth control pills. The feasibility and effectiveness of web-based education targeting post-anesthesia care unit (PACU) nurses are limited. PACU nurses are responsible for patient education related to the interaction between Sugammadex and hormonal contraceptives.
Purpose
The purpose of the project is to examine the effect of a web-based education on PACU nurses’ awareness and knowledge about the interaction between Sugammadex and hormonal contraceptives.
Methods
A quantitative, quasi-experimental, pre-/post-test design study was conducted among PACU nurses who work at a healthcare facility located in the southeast region of the United States. An online survey was used to investigate the effect of web-based education on PACU nurses’ knowledge about the interaction between Sugammadex and hormonal contraceptives.
Results
Among 27 PACU nurses, their years of experience ranged from 0 to 26 years (4.43 ± 6.65). There was a significant pretest-posttest difference on Question 1 (the mechanism of action of Sugammadex) (χ 2 (1) = 6.22, p = .013). The average number of correct answers increased from 4.00 ± 0.87 to 4.70 ± 0.54 (t = 3.99, p < .001).
Conclusion
The web-based education was effective in improving PACU nurses’ knowledge of drug interactions. This finding may contribute to the development of a standardized online education program for anesthesia providers, enhancing their skills and competence in providing patient education on anesthetic agents.
Pesticide use has reached alarming levels globally, causing potential risks to human health and the environment. With its high population densities and rapid development, California emerges as one of the country's leading users of pesticides in the country. Recognized point and non-point pathways for pesticides entering surface water include mixed indoor and outdoor applications and treated municipal wastewater effluent, indicating that conventional wastewater treatment plants (WWTPs) treatment processes are inefficient at removing pesticides from effluents. Recent studies have assessed the fate of pesticides in surface water with a limited understanding of watershed characteristics. This project aimed to quantify WWTP discharges, a lesser-known source of pesticide loads, and investigate the potential environmental benefits of their removal. To evaluate WWTP pesticide concentrations, we developed a geospatial model with municipal WWTP discharges, streamflow characteristics, and pesticide loading data to estimate pesticide concentrations within wastewater receiving streams. Next, we set a multimetric Pesticide Vulnerability Index (PVI) to identify the most vulnerable California watersheds to wastewater-derived pesticide loading. Finally, we investigated the environmental benefits of incorporating advanced WWTP processes for pesticide residue removal from treated effluent before surface water discharge using estimated WWTP life-cycle costs. This work presents an integrated assessment of pesticides in surface water to support source control and mitigation efforts. It highlighted the significance and effects of municipal WWTP pesticide loading in California’s urban waterways. In addition, completing this project provided insight into the environmental and economic costs associated with municipal wastewater-derived pesticide mitigation.
Personalized Learning Instruction (PLI) is the practice of personalizing instructional practices, scaffolding, and assessing the schoolwork of each individual student based on their specific learning needs and the standards of the curricular content. It involves student choice and interest within a flexible structure. Currently, most of the research that has been conducted on PLI has focused on math instruction, older secondary students (grades 9-12), and relatively small samples of students. Little research has been conducted to determine if and what impacts PLI may or may not have on English Language Arts achievement. Nor has much emphasis been placed on middle schoolers, where routines and patterns for future success in secondary school are established. The purpose of this study was to understand middle school principals’ perceptions of (1) Personalized Learning Instruction (PLI), (2) the effects of Personalized Learning Instruction on middle grades English Language Arts achievement, and (3) the impact of COVID-19 on the implementation of Personalized Learning Instruction in their schools. This qualitative case study involved in-depth interviews of four middle school principals who had experience with the implementation of PLI in their schools. Four themes emerged from these case studies and are expressed through thematic sentences; (1) Principals perceive a positive impact on student achievement through Personalized Learning Instruction, largely through increased engagement with reading in English Language Arts classes, (2) A misalignment exists between using PLI strategies and current instructional practices, (3) Staffing issues, inexperience, and vacancies have pushed instructional leaders away from Personalized Learning Instruction, and (4) Personalized Learning Instruction is not a priority post-COVID-19.
Group purchasing (GP) is a procurement strategy by which the retailers can negotiate better prices by increasing their negotiation power through collaboration with each other. GP problem can be modeled as a generalized newsvendor problem, although it is more realistic to model this problem with stochastic demand, current literature on GP is mostly focused on problems with deterministic demand. Comparing the single retailer newsvendor vs. a newsvendor problem with multiple retailers, there has been more attention paid to the newsvendor problem with single retailer. When there are multiple retailers, competition would be another important aspect to consider, which is lacking in parts of the literature and will be considered in this research. Different contracting scenarios such as revenue-sharing and buyback contracts are other aspects which can be considered in the GP problem which has not been studied so far. Given that; four research questions are defined to investigate in this study: 1) the first question investigates the newsvendor problem with quantity discount pricing from supplier by exploring an analytical approach to solve this problem building on existing solutions from the literature; next a second novel solution approach is proposed which solves the problem in fewer steps; answering this question makes the foundation for our subsequent research questions. 2) the second research question studies the GP problem with multiple symmetric retailers; this research question is an extension of the first research question which investigates the GP supply chain consisting of multiple symmetric retailers. 3) third research question explores the solution to GP with multiple asymmetric retailers and suppliers; since this problem is complex to solve, the GP problem is divided into two sub-problems, retailers’ problem, and suppliers’ problem which are solved separately and then brought together to provide an answer to the overall GP problem, and 4) finally, fourth research question introduces different supply chain contracts to the GP problem and investigates studying the effect of these contracts on the retailers’ profit. Mathematical results as well as managerial insights are provided for each model through sensitivity analysis and numerical experiments.
Materials science aims to explore the properties and behaviors of different materials, from metals to advanced carbon structures. This dissertation focuses on three distinct areas of study: Inconel Alloy 740H, polycrystalline graphene, and tetragraphene (TG).
The first part of this work concentrates on developing and validating a Chaboche unified constitutive model. This model incorporates both nonlinear isotropic and kinematic hardening rules to accurately predict the stress-strain behavior of Inconel Alloy 740H, a high-temperature nickel-based superalloy. The material parameters of the model are determined and its accuracy validated through experimental data obtained from uniaxial strain-controlled loading tests across a wide temperature and strain ranges.
The second part explores the mechanical properties of polycrystalline graphene, bridging scales from nanoscale to macroscale through a multiscale molecular dynamics (MD)–finite element (FE) modeling approach. By studying the behavior of graphene sheets with different grain boundaries and atomic structures, insights are gained into the influence of grain size on mechanical properties like the Young modulus and fracture stress.
The third part of this dissertation investigates the mechanical properties of tetragraphene (TG), a quasi-2D semiconductor carbon allotrope, with a focus on addressing graphene's limitations in electronic applications. Through MD simulations, the research examines TG's fracture properties under mixed mode I and II loading, considering variables such as loading phase angle, crack structure, and temperature.
Since the early 1980s, American educational reformers have tried to improve schools through standards, high-stakes tests, and punishments for those schools that failed to meet the mark. In North Carolina, many schools with diverse populations and low socioeconomic status have struggled to succeed, receiving the state performance grade of D or F and the consequent “low-performing” label. Meanwhile, some teachers in these schools have achieved at high levels and attempted to improve not only their classrooms, but their schools and districts. Few researchers have sought the opinions and expertise of high-performing teachers in order to better understand their experiences, their role as change agents, and their recommendations for other so-called “low-performing” schools. This qualitative multiple case study used in-depth interviews with these high-performing teachers in “low-performing” elementary schools in North Carolina. Specifically, this research gathered information about their backgrounds, their actions for school transformation, and their lessons learned about education and equity. Findings from the study indicated that high-performing elementary teachers tried to reform their “low-performing” schools through teacher agency but were blocked by multiple factors. School administrators and district officials reduced teacher agency and opportunities for school improvement. North Carolina’s “low-performing” schools policy harmed children, reinforced school failure, and produced discriminatory and inequitable results. Teacher agency theory provided a promising approach for the state to change course and improve failing schools.
Recent advancements in deep learning have significantly propelled the field of computer vision, especially in 3D human model recovery from monocular images. This work is centered on developing efficient deep learning models for digitizing human subjects, thereby laying a solid foundation for various subsequent applications. 3D human mesh estimation from monocular images often requires complex deep learning models. In addressing this, we propose a hybrid approach combining deep learning models with analytical inverse kinematics to precisely estimate 3D pose and shape.
Our precise 3D pose estimations facilitate three high-impact downstream applications. Firstly, we aim to create a real-time biomechanics analysis system that provides low-cost, real-time, and accurate estimations of kinematic sequences for managing joint human health-performance. Herein, our system integrates mobile modular 3D pose estimation with model-based inverse kinematics optimization seamlessly. The next downstream task entails skeleton-based human action recognition (HAR), with extensive applications in smart homes, cities, and retail. By rendering 3D pose sequences as RGB images and utilizing conventional CNN architectures alongside various data augmentation schemes, we have achieved results comparable to sophisticated Graph Neural Network models. Lastly, in scenarios where visual cues are scarce yet human monitoring is essential, radar-based sensing offers a non-intrusive solution for tracking human movements and vital signs. Given the paucity of extensive radar datasets, we introduce a "virtual radar" framework in our third downstream task. This framework, driven by 3D pose and physics-informed principles, generates synthetic radar data, presenting a novel avenue towards a nuanced understanding of human behavior through privacy-preserving radar-based methodologies.
